EurekaMag Full Text Articles Chapter 55,100

MacNeil, J.A.M.; Adachi, J.D.; Goltzman, D.; Josse, R.G.; Kovacs, C.S.; Prior, J.C.; Olszynski, W.; Davison, K.S.; Kaiser, S.M. 2012: Predicting fracture using 2D finite element modelling. Medical Engineering and Physics 34(4): 478-484

Sambrook, P.N.; Flahive, J.; Hooven, F.H.; Boonen, S.; Chapurlat, R.; Lindsay, R.; Nguyen, T.V.; Díez-Perez, A.; Pfeilschifter, J.; Greenspan, S.L.; Hosmer, D.; Netelenbos, J.Coen.; Adachi, J.D.; Watts, N.B.; Cooper, C.; Roux, C.; Rossini, M.; Siris, E.S.; Silverman, S.; Saag, K.G.; Compston, J.E.; LaCroix, A.; Gehlbach, S. 2011: Predicting fractures in an international cohort using risk factor algorithms without BMD. Journal of bone and mineral research: the official journal of the American Society for Bone and Mineral Research 26(11): 2770-2777

Haider, M.K.; Bertrand, H.-O.; Hubbard, R.E. 2011: Predicting fragment binding poses using a combined MCSS MM-GBSA approach. Journal of Chemical Information and Modeling 51(5): 1092-1105

Mathias, J.M. 2010: Predicting frailty risk in older patients. Or Manager 26(11): 23-24

Abramson, D.H.; Folberg, R.; Francis, J.H. 2018: Clinicopathologic Correlation of Choroidal Invasion in Retinoblastoma. Ophthalmology 125(4): 568

Bouarfa, L.; Atallah, L.; Kwasnicki, R.M.; Pettitt, C.; Frost, G.; Yang, G.-Z. 2014: Predicting free-living energy expenditure using a miniaturized ear-worn sensor: an evaluation against doubly labeled water. IEEE Transactions on Bio-Medical Engineering 61(2): 566-575

Bracewell, S.A.; Robinson, L.A.; Firth, L.B.; Knights, A.M. 2013: Predicting free-space occupancy on novel artificial structures by an invasive intertidal barnacle using a removal experiment. Plos one 8(9): E74457

Gale, C.P.; Cattle, B.A.; Simms, A.D.; Greenwood, D.C.; West, R.M. 2009: Predicting freedom from clinical events in non-ST-elevation acute coronary syndromes. Heart 95(16): 1355; Author Reply 1355-6

Brieger, D.; Fox, K.A.A.; Fitzgerald, G.; Eagle, K.A.; Budaj, A.; Avezum, A.; Granger, C.B.; Costa, B.; Anderson, F.A.; Steg, P.G. 2009: Predicting freedom from clinical events in non-ST-elevation acute coronary syndromes: the Global Registry of Acute Coronary Events. Heart 95(11): 888-894

Khrapak, S.A.; Morfill, G.E. 2009: Predicting freezing for some repulsive potentials. Physical Review Letters 103(25): 255003

Feng, G.; Foster, D.H. 2012: Predicting frequency of metamerism in natural scenes by entropy of colors. Journal of the Optical Society of America. a Optics Image Science and Vision 29(2): A200-A208

Bauminger, N.; Solomon, M.; Rogers, S.J. 2010: Predicting friendship quality in autism spectrum disorders and typical development. Journal of Autism and Developmental Disorders 40(6): 751-761

De Vries, H.; Eggers, S.M.; Lechner, L.; van Osch, L.; van Stralen, M.M. 2014: Predicting fruit consumption: the role of habits, previous behavior and mediation effects. Bmc Public Health 14: 730

Chang, S.; Wang, Z.J. 2014: Predicting fruit fly's sensing rate with insect flight simulations. Proceedings of the National Academy of Sciences of the United States of America 111(31): 11246-11251

Lee, S.; Dong, D.X.; Jindal, R.; Maguire, T.; Mitra, B.; Schloss, R.; Yarmush, M. 2014: Predicting full thickness skin sensitization using a support vector machine. Toxicology in Vitro: An International Journal Published in Association with Bibra 28(8): 1413-1423

Greenaway, M.C.; Duncan, N.L.; Hanna, S.; Smith, G.E. 2012: Predicting functional ability in mild cognitive impairment with the Dementia Rating Scale-2. International Psychogeriatrics 24(6): 987-993

De Saint-Hubert, M.; Schoevaerdts, D.; Cornette, P.; D'Hoore, W.; Boland, B.; Swine, C. 2010: Predicting functional adverse outcomes in hospitalized older patients: a systematic review of screening tools. Journal of Nutrition Health and Aging 14(5): 394-399

Lu, H.; Lin, L.; Sato, S.; Xing, Y.; Lee, C.J. 2009: Predicting functional alternative splicing by measuring RNA selection pressure from multigenome alignments. Plos Computational Biology 5(12): E1000608

Veeramani, B.; Bader, J.S. 2010: Predicting functional associations from metabolism using bi-partite network algorithms. Bmc Systems Biology 4: 95

Zhang, T.; Guo, L.; Li, K.; Zhu, D.; Cui, G.; Liu, T. 2011: Predicting functional brain ROIs via fiber shape models. Medical Image Computing and Computer-Assisted Intervention: Miccai . International Conference on Medical Image Computing and Computer-Assisted Intervention 14(Part 2): 42-49

Coleman, A.; Weir, K.; Ware, R.S.; Boyd, R. 2015: Predicting functional communication ability in children with cerebral palsy at school entry. Developmental Medicine and Child Neurology 57(3): 279-285

Josephs, K.A.; Whitwell, J.L.; Weigand, S.D.; Senjem, M.L.; Boeve, B.F.; Knopman, D.S.; Smith, G.E.; Ivnik, R.J.; Jack, C.R.; Petersen, R.C. 2011: Predicting functional decline in behavioural variant frontotemporal dementia. Brain: a Journal of Neurology 134(Part 2): 432-448

Hoogerduijn, J.G.; de Rooij, S.E.; Grobbee, D.E.; Schuurmans, M.J. 2014: Predicting functional decline in older patients undergoing cardiac surgery. Age and Ageing 43(2): 218-221

Chen, L-Yu.; Liu, L-Kuo.; Liu, C-Liang.; Peng, L-Ning.; Lin, M-Hsien.; Chen, L-Kung.; Lan, C-Fu.; Chang, P-Lun. 2013: Predicting functional decline of older men living in veteran homes by minimum data set: implications for disability prevention programs in long term care settings. Journal of the American Medical Directors Association 14(4): 309.E9

Adzhubei, I.; Jordan, D.M.; Sunyaev, S.R. 2013: Predicting functional effect of human missense mutations using PolyPhen-2. Current Protocols in Human Genetics Chapter 7: Unit 7.20

Lindenberg, R.; Zhu, L.L.; Rüber, T.; Schlaug, G. 2012: Predicting functional motor potential in chronic stroke patients using diffusion tensor imaging. Human Brain Mapping 33(5): 1040-1051

Reid, J.M.; Gubitz, G.J.; Dai, D.; Kydd, D.; Eskes, G.; Reidy, Y.; Christian, C.; Counsell, C.E.; Dennis, M.; Phillips, S.J. 2010: Predicting functional outcome after stroke by modelling baseline clinical and CT variables. Age and Ageing 39(3): 360-366

Nijboer, T.; van de Port, I.; Schepers, V.; Post, M.; Visser-Meily, A. 2013: Predicting functional outcome after stroke: the influence of neglect on basic activities in daily living. Frontiers in Human Neuroscience 7: 182

Simons, M.A.; Ziviani, J.; Copley, J. 2010: Predicting functional outcome for children on admission after burn injury: do parents hold the key?. Journal of Burn Care and Research: Official Publication of the American Burn Association 31(5): 750-765

Li, C.-C.; Chen, Y.-M.; Tsay, S.-L.; Hu, G.-C.; Lin, K.-C. 2010: Predicting functional outcomes in patients suffering from ischaemic stroke using initial admission variables and physiological data: a comparison between tree model and multivariate regression analysis. Disability and Rehabilitation 32(25): 2088-2096

Bade, M.J.; Kittelson, J.M.; Kohrt, W.M.; Stevens-Lapsley, J.E. 2014: Predicting functional performance and range of motion outcomes after total knee arthroplasty. American Journal of Physical Medicine and Rehabilitation 93(7): 579-585

O'Connor, S.R.; Bleakley, C.M.; Tully, M.A.; McDonough, S.M. 2013: Predicting functional recovery after acute ankle sprain. Plos one 8(8): E72124

Matsuzaki, H.; Narisawa, H.; Miwa, H.; Toishi, S. 2009: Predicting functional recovery and return to work after mutilating hand injuries: usefulness of Campbell's Hand Injury Severity Score. Journal of Hand Surgery 34(5): 880-885

Friedrich, M.G. 2013: Predicting functional recovery in acute heart disease using a single snapshot: we have a winner. Journal of the American College of Cardiology 61(1): 64-65

Torkamani, A.; Schork, N.J. 2008: Predicting functional regulatory polymorphisms. Bioinformatics 24(16): 1787-1792

Haddow, C.; Perry, J.; Durrant, M.; Faith, J. 2011: Predicting functional residues of protein sequence alignments as a feature selection task. International Journal of Data Mining and Bioinformatics 5(6): 691-705

Skinner, M.L.; Haggerty, K.P.; Fleming, C.B.; Catalano, R.F. 2009: Predicting functional resilience among young-adult children of opiate-dependent parents. Journal of Adolescent Health: Official Publication of the Society for Adolescent Medicine 44(3): 283-290

McKenzie, H.A.; Fung, C.; Becker, T.M.; Irvine, M.; Mann, G.J.; Kefford, R.F.; Rizos, H. 2010: Predicting functional significance of cancer-associated p16(INK4a) mutations in CDKN2A. Human Mutation 31(6): 692-701

Ward, L.D.; Bussemaker, H.J. 2008: Predicting functional transcription factor binding through alignment-free and affinity-based analysis of orthologous promoter sequences. Bioinformatics 24(13): I165-I171

Selpi; Bryant, C.H.; Kemp, G.J.L.; Sarv, J.; Kristiansson, E.; Sunnerhagen, P. 2009: Predicting functional upstream open reading frames in Saccharomyces cerevisiae. Bmc Bioinformatics 10: 451

Ucar, D.; Beyer, A.; Parthasarathy, S.; Workman, C.T. 2009: Predicting functionality of protein-DNA interactions by integrating diverse evidence. Bioinformatics 25(12): I137-I144

Levenstien, M.A.; Klein, R.J. 2011: Predicting functionally important SNP classes based on negative selection. Bmc Bioinformatics 12: 26

Dwyer, R.S.; Ricci, D.P.; Colwell, L.J.; Silhavy, T.J.; Wingreen, N.S. 2013: Predicting functionally informative mutations in Escherichia coli BamA using evolutionary covariance analysis. Genetics 195(2): 443-455

Hu, L.; Huang, T.; Shi, X.; Lu, W.-C.; Cai, Y.-D.; Chou, K.-C. 2011: Predicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid properties. Plos one 6(1): E14556

Shah, D.A.; Molineros, J.E.; Paul, P.A.; Willyerd, K.T.; Madden, L.V.; De Wolf, E.D. 2013: Predicting fusarium head blight epidemics with weather-driven pre- and post-anthesis logistic regression models. Phytopathology 103(9): 906-919

Dunn, M.; Mazanov, J.; Sitharthan, G. 2009: Predicting future anabolic-androgenic steroid use intentions with current substance use: findings from an internet-based survey. Clinical Journal of Sport Medicine: Official Journal of the Canadian Academy of Sport Medicine 19(3): 222-227

Hansel, N.N.; Matsui, E.C.; Rusher, R.; McCormack, M.C.; Curtin-Brosnan, J.; Peng, R.D.; Mazique, D.; Breysse, P.N.; Diette, G.B. 2011: Predicting future asthma morbidity in preschool inner-city children. Journal of Asthma: Official Journal of the Association for the Care of Asthma 48(8): 797-803

Siegenthaler, U.; Oeschger, H. 1978: Predicting future atmospheric carbon dioxide levels. Science 199(4327): 388-395

Tolan, T. 2011: Predicting future behavior. Healthcare Informatics: the Business Magazine for Information and Communication Systems 28(6): 80

Lau, E.H.Y.; He, X.-Q.; Lee, C.-K.; Wu, J.T. 2013: Predicting future blood demand from thalassemia major patients in Hong Kong. Plos one 8(12): E81846

Kuceyeski, A.; Kamel, H.; Navi, B.B.; Raj, A.; Iadecola, C. 2014: Predicting future brain tissue loss from white matter connectivity disruption in ischemic stroke. Stroke 45(3): 717-722

Zhang, D.; Shen, D. 2012: Predicting future clinical changes of MCi patients using longitudinal and multimodal biomarkers. Plos one 7(3): E33182

Bewick, S.; Stuble, K.L.; Lessard, J-Phillipe.; Dunn, R.R.; Adler, F.R.; Sanders, N.J. 2014: Predicting future coexistence in a North American ant community. Ecology and evolution 4(10): 1804-1819

Petrič, I.; Cestnik, B. 2014: Predicting future discoveries from current scientific literature. Methods in Molecular Biology 1159: 159-168

Ordonez-Llanos, J.; Sionis, A. 2013: Predicting future events in patients with stable cardiovascular disease. Will high-sensitivity cardiac troponins be up to the challenge?. Clinical Biochemistry 46(1-2): 10-11

Furukawa, K.; Cologne, J.B.; Shimizu, Y.; Ross, N.Phillip. 2009: Predicting future excess events in risk assessment. Risk Analysis: An Official Publication of the Society for Risk Analysis 29(6): 885-899

Corke, C.; Leeuw, E.d.; Lo, S.K.; George, C. 2009: Predicting future intensive care demand in Australia. Critical Care and Resuscitation: Journal of the Australasian Academy of Critical Care Medicine 11(4): 257-260

De Grey, A.D.N.J. 2014: Predicting future longevity: at long last, extrapolation is on the way out. Rejuvenation Research 17(3): 247-248

Bagci, U.; Yao, J.; Miller-Jaster, K.; Chen, X.; Mollura, D.J. 2013: Predicting future morphological changes of lesions from radiotracer uptake in 18F-FDG-PET images. Plos one 8(2): E57105

Gray, N.S.; Fitzgerald, S.; Taylor, J.; Macculloch, M.J.; Snowden, R.J. 2007: Predicting future reconviction in offenders with intellectual disabilities: the predictive efficacy of VRAG, PCL-SV, and the HCR-20. Psychological Assessment 19(4): 474-479

Curtis, J.R.; Luijtens, K.; Kavanaugh, A. 2012: Predicting future response to certolizumab pegol in rheumatoid arthritis patients: features at 12 weeks associated with low disease activity at 1 year. Arthritis Care and Research 64(5): 658-667

Thamrin, C.; Zindel, J.; Nydegger, R.; Reddel, H.K.; Chanez, P.; Wenzel, S.E.; FitzPatrick, S.; Watt, R.A.; Suki, B.él.; Frey, U. 2011: Predicting future risk of asthma exacerbations using individual conditional probabilities. Journal of Allergy and Clinical Immunology 127(6): 1494-502.E3

Van Voorhees, B.W.; Paunesku, D.; Gollan, J.; Kuwabara, S.; Reinecke, M.; Basu, A. 2008: Predicting future risk of depressive episode in adolescents: the Chicago Adolescent Depression Risk Assessment (CADRA). Annals of Family Medicine 6(6): 503-511

Hermosillo, R.; Ritterband-Rosenbaum, A.; van Donkelaar, P. 2011: Predicting future sensorimotor states influences current temporal decision making. Journal of Neuroscience: the Official Journal of the Society for Neuroscience 31(27): 10019-10022

Kanoupakis, E.M.; Manios, E.G.; Vardas, P.E. 2009: Predicting future shocks in implantable cardioverter defibrillator recipients: the role of biomarkers. Europace: European Pacing Arrhythmias and Cardiac Electrophysiology: Journal of the Working Groups on Cardiac Pacing Arrhythmias and Cardiac Cellular Electrophysiology of the European Society of Cardiology 11(11): 1434-1439

Mitchell, C.C.; Ashley, S.W.; Zinner, M.J.; Moore, F.D. 2007: Predicting future staffing needs at teaching hospitals: use of an analytical program with multiple variables. Archives of Surgery 142(4): 329-334

May, A.M.; Klonsky, E.D.; Klein, D.N. 2012: Predicting future suicide attempts among depressed suicide ideators: a 10-year longitudinal study. Journal of Psychiatric Research 46(7): 946-952

Coid, J.W.; Ullrich, S.; Kallis, C. 2013: Predicting future violence among individuals with psychopathy. British Journal of Psychiatry: the Journal of Mental Science 203(5): 387-388

Liew, T.M.; Luo, N.; Ng, W.Y.; Chionh, H.L.; Goh, J.; Yap, P. 2010: Predicting gains in dementia caregiving. Dementia and Geriatric Cognitive Disorders 29(2): 115-122

Pagani, L.S.; Derevensky, J.L.; Japel, C. 2009: Predicting gambling behavior in sixth grade from kindergarten impulsivity: a tale of developmental continuity. Archives of Pediatrics and Adolescent Medicine 163(3): 238-243

Lu, W.C.; Cheng, C.-F.; Chen, L.H. 2013: Predicting game-attending behavior in amateur athletes: the moderating role of intention stability. Psychological Reports 113(2): 420-434

Wu, B.; Buddensick, T.J.; Ferdosi, H.; Narducci, D.Marie.; Sautter, A.; Setiawan, L.; Shaukat, H.; Siddique, M.; Sulkowski, G.N.; Kamangar, F.; Kowdley, G.C.; Cunningham, S.C. 2014: Predicting gangrenous cholecystitis. Hpb: the Official Journal of the International Hepato Pancreato Biliary Association 16(9): 801-806

Siderius, D.W.; Gelb, L.D. 2009: Predicting gas adsorption in complex microporous and mesoporous materials using a new density functional theory of finely discretized lattice fluids. Langmuir 25(3): 1296-1299

Guerra, D.; Ricciardi, L.; Laborde, J-Claude.; Domenech, S. 2007: Predicting gaseous pollutant dispersion around a workplace. Journal of Occupational and Environmental Hygiene 4(8): 619-633

Li, C.; Xie, H.-B.; Chen, J.; Yang, X.; Zhang, Y.; Qiao, X. 2014: Predicting gaseous reaction rates of short chain chlorinated paraffins with ·OH: overcoming the difficulty in experimental determination. Environmental Science and Technology 48(23): 13808-13816

Paproski, R.J.; Young, J.D.; Cass, C.E. 2010: Predicting gemcitabine transport and toxicity in human pancreatic cancer cell lines with the positron emission tomography tracer 3'-deoxy-3'-fluorothymidine. Biochemical Pharmacology 79(4): 587-595

Joyce, A.R.; Palsson, B.Ø 2008: Predicting gene essentiality using genome-scale in silico models. Methods in Molecular Biology 416: 433-457

Costa, I.G.; Roider, H.G.; do Rego, T.G.; de Carvalho, F.d.A.T. 2011: Predicting gene expression in T cell differentiation from histone modifications and transcription factor binding affinities by linear mixture models. Bmc Bioinformatics 12(Suppl 1): S29

Bhosale, R.; Jewell, J.B.; Hollunder, J.; Koo, A.J.K.; Vuylsteke, M.; Michoel, T.; Hilson, P.; Goossens, A.; Howe, G.A.; Browse, J.; Maere, S. 2013: Predicting gene function from uncontrolled expression variation among individual wild-type Arabidopsis plants. Plant Cell 25(8): 2865-2877

Guan, Y.; Myers, C.L.; Hess, D.C.; Barutcuoglu, Z.; Caudy, A.A.; Troyanskaya, O.G. 2008: Predicting gene function in a hierarchical context with an ensemble of classifiers. Genome Biology 9(Suppl 1): S3

Chen, Y.; Li, Z.; Wang, X.; Feng, J.; Hu, X. 2010: Predicting gene function using few positive examples and unlabeled ones. Bmc Genomics 11(Suppl 2): S11

Phuong, T.; Nhung, N. 2013: Predicting gene function using similarity learning. Bmc Genomics 14(Suppl 4): S4

Cohen, P.E.; Holloway, J.K. 2010: Predicting gene networks in human oocyte meiosis. Biology of Reproduction 82(3): 469-472

Kuppuswamy, U.; Ananthasubramanian, S.; Wang, Y.; Balakrishnan, N.; Ganapathiraju, M.K. 2014: Predicting gene ontology annotations of orphan GWAS genes using protein-protein interactions. Algorithms for Molecular Biology: Amb 9(1): 10

Dozmorov, M.G.; Giles, C.B.; Wren, J.D. 2011: Predicting gene ontology from a global meta-analysis of 1-color microarray experiments. Bmc Bioinformatics 12(Suppl 10): S14

Zhu, M.; Dahmen, J.L.; Stacey, G.; Cheng, J. 2013: Predicting gene regulatory networks of soybean nodulation from RNA-Seq transcriptome data. Bmc Bioinformatics 14: 278

Cosgrove, E.J.; Zhou, Y.; Gardner, T.S.; Kolaczyk, E.D. 2008: Predicting gene targets of perturbations via network-based filtering of mRNA expression compendia. Bioinformatics 24(21): 2482-2490

Teif, V.B. 2010: Predicting gene-regulation functions: lessons from temperate bacteriophages. Biophysical Journal 98(7): 1247-1256

Rahmani, H.; Blockeel, H.; Bender, A. 2012: Predicting genes involved in human cancer using network contextual information. Journal of Integrative Bioinformatics 9(1): 210

Feng, Y.; Chiu, C.-H. 2014: Predicting genetic and ecological characteristics of bacterial species by comparing the trajectories of dN/dS and dI/dS in bacterial genomes. Molecular Biosystems 10(2): 266-272

Chipman, K.C.; Singh, A.K. 2009: Predicting genetic interactions with random walks on biological networks. Bmc Bioinformatics 10: 17

Schooler, C.; Revell, A.J.; Timpano, K.R.; Wheaton, M.; Murphy, D.L. 2008: Predicting genetic loading from symptom patterns in obsessive- compulsive disorder: a latent variable analysis. Depression and Anxiety 25(8): 680-688

Raadsma, H.W.; Moser, G.; Crump, R.E.; Khatkar, M.S.; Zenger, K.R.; Cavanagh, J.A.L.; Hawken, R.J.; Hobbs, M.; Barris, W.; Solkner, J.; Nicholas, F.W.; Tier, B. 2008: Predicting genetic merit for mastitis and fertility in dairy cattle using genome wide selection and high density SNP screens. Developments in Biologicals 132: 219-223

Lee, I.; Lehner, B.; Vavouri, T.; Shin, J.; Fraser, A.G.; Marcotte, E.M. 2010: Predicting genetic modifier loci using functional gene networks. Genome Research 20(8): 1143-1153

Sahu, S.S.; Weirick, T.; Kaundal, R. 2014: Predicting genome-scale Arabidopsis-Pseudomonas syringae interactome using domain and interolog-based approaches. Bmc Bioinformatics 15(Suppl 11): S13

Yang, N.; Winkel, L.H.E.; Johannesson, K.H. 2014: Predicting geogenic arsenic contamination in shallow groundwater of south Louisiana, United States. Environmental Science and Technology 48(10): 5660-5666

Sandoval-Ruiz, C.A.; Zumaquero-Rios, J.L.; Rojas-Soto, O.R. 2008: Predicting geographic and ecological distributions of triatomine species in the southern Mexican state of Puebla using ecological niche modeling. Journal of Medical Entomology 45(3): 540-546

Epp, T.Y.; Waldner, C.L.; Berke, O. 2009: Predicting geographical human risk of West Nile virus--Saskatchewan, 2003 and 2007. Canadian Journal of Public Health 100(5): 344-348

Rocha, C.M.; Kruger, E.; Whyman, R.; Tennant, M. 2014: Predicting geographically distributed adult dental decay in the greater Auckland region of New Zealand. Community Dental Health 31(2): 85-90

Carpenter, C.R.; Avidan, M.S.; Wildes, T.; Stark, S.; Fowler, S.A.; Lo, A.X. 2014: Predicting geriatric falls following an episode of emergency department care: a systematic review. Academic Emergency Medicine: Official Journal of the Society for Academic Emergency Medicine 21(10): 1069-1082

Reboul, C.F.; Mahmood, K.; Whisstock, J.C.; Dunstone, M.A. 2012: Predicting giant transmembrane β-barrel architecture. Bioinformatics 28(10): 1299-1302

Hamerton, I.; Howlin, B.J.; Kamyszek, G. 2012: Predicting glass transition temperatures of polyarylethersulphones using QSPR methods. Plos one 7(6): E38424

Howard, D.L.; Kim, M.M.; Hartnett, M.E. 2011: Predicting glaucoma diagnosis in an elderly sample: revisiting the established populations for epidemiologic studies of the elderly. Journal of the National Medical Association 103(4): 332-341

Bowd, C.; Lee, I.; Goldbaum, M.H.; Balasubramanian, M.; Medeiros, F.A.; Zangwill, L.M.; Girkin, C.A.; Liebmann, J.M.; Weinreb, R.N. 2012: Predicting glaucomatous progression in glaucoma suspect eyes using relevance vector machine classifiers for combined structural and functional measurements. Investigative Ophthalmology and Visual Science 53(4): 2382-2389

Xiang, Y.; Zhang, C.-Q.; Huang, K. 2012: Predicting glioblastoma prognosis networks using weighted gene co-expression network analysis on TCGA data. Bmc Bioinformatics 13(Suppl 2): S12

Trautwein, U.; Lüdtke, O. 2007: Predicting global and topic-specific certainty beliefs: domain-specificity and the role of the academic environment. British Journal of Educational Psychology 77(Part 4): 907-934

DeMott, P.J.; Prenni, A.J.; Liu, X.; Kreidenweis, S.M.; Petters, M.D.; Twohy, C.H.; Richardson, M.S.; Eidhammer, T.; Rogers, D.C. 2010: Predicting global atmospheric ice nuclei distributions and their impacts on climate. Proceedings of the National Academy of Sciences of the United States of America 107(25): 11217-11222

Gustafson, E.J.; Shvidenko, A.Z.; Sturtevant, B.R.; Scheller, R.M. 2010: Predicting global change effects on forest biomass and composition in south-central Siberia. Ecological Applications: a Publication of the Ecological Society of America 20(3): 700-715

Senior, K. 2008: Predicting global death patterns not an easy task. Lancet. Infectious Diseases 8(7): 411

Tangri, N.; Alam, A.; Giannetti, N.; Deedwardes, M.B.; Cantarovich, M. 2008: Predicting glomerular filtration rate in heart transplant recipients using serum creatinine-based equations with cimetidine. Journal of Heart and Lung Transplantation: the Official Publication of the International Society for Heart Transplantation 27(8): 905-909

Novak, M.T.; Yuan, F.; Reichert, W.M. 2013: Predicting glucose sensor behavior in blood using transport modeling: relative impacts of protein biofouling and cellular metabolic effects. Journal of Diabetes Science and Technology 7(6): 1547-1560

McDowell, G.S.V.; Blanchard, A.P.; Taylor, G.P.; Figeys, D.; Fai, S.; Bennett, S.A.L. 2014: Predicting glycerophosphoinositol identities in lipidomic datasets using VaLID (Visualization and Phospholipid Identification)--an online bioinformatic search engine. Biomed Research International 2014: 818670

Fluckiger, M.; Brown, M.R.; Ward, L.R.; Moltschaniwskyj, N.A. 2011: Predicting glycogen concentration in the foot muscle of abalone using near infrared reflectance spectroscopy (NIRS). Food Chemistry 126(4): 1817-1820

O'Brien, S.R.; Xue, Y. 2014: Predicting goal achievement during stroke rehabilitation for Medicare beneficiaries. Disability and Rehabilitation 36(15): 1273-1278

Wurm, M.F.; Hrkać, M.; Morikawa, Y.; Schubotz, R.I. 2014: Predicting goals in action episodes attenuates BOLD response in inferior frontal and occipitotemporal cortex. Behavioural Brain Research 274: 108-117

Xu, B.; Madix, R.J.; Friend, C.M. 2014: Predicting gold-mediated catalytic oxidative-coupling reactions from single crystal studies. Accounts of Chemical Research 47(3): 761-772

Robertson, J.M.; Söhn, M.; Yan, D. 2010: Predicting grade 3 acute diarrhea during radiation therapy for rectal cancer using a cutoff-dose logistic regression normal tissue complication probability model. International Journal of Radiation Oncology Biology Physics 77(1): 66-72

Duval, B.D.; Anderson-Teixeira, K.J.; Davis, S.C.; Keogh, C.; Long, S.P.; Parton, W.J.; DeLucia, E.H. 2013: Predicting greenhouse gas emissions and soil carbon from changing pasture to an energy crop. Plos one 8(8): E72019

Magnusson, M.; Bergsten, A.; Ecke, F.; Bodin, O.; Bodin, L.; Hörnfeldt, B. 2013: Predicting grey-sided vole occurrence in northern Sweden at multiple spatial scales. Ecology and Evolution 3(13): 4365-4376

Wasson, P.; Prodoehl, J.; Coombes, S.A.; Corcos, D.M.; Vaillancourt, D.E. 2010: Predicting grip force amplitude involves circuits in the anterior basal ganglia. Neuroimage 49(4): 3230-3238

Mowat, G.; Heard, D.C.; Schwarz, C.J. 2013: Predicting grizzly bear density in western North America. Plos one 8(12): E82757

Peterson, C.B.; Crosby, R.D.; Wonderlich, S.A.; Mitchell, J.E.; Crow, S.J.; Engel, S. 2013: Predicting group cognitive-behavioral therapy outcome of binge eating disorder using empirical classification. Behaviour Research and Therapy 51(9): 526-532

Croon, M.A.; van Veldhoven, M.J.P.M. 2007: Predicting group-level outcome variables from variables measured at the individual level: a latent variable multilevel model. Psychological Methods 12(1): 45-57

Busscher, I.; Wapstra, F.H.; Veldhuizen, A.G. 2010: Predicting growth and curve progression in the individual patient with adolescent idiopathic scoliosis: design of a prospective longitudinal cohort study. Bmc Musculoskeletal Disorders 11: 93

Mesman, J.; Stoel, R.; Bakermans-Kranenburg, M.J.; van IJzendoorn, M.H.; Juffer, F.; Koot, H.M.; Alink, L.R.A. 2009: Predicting growth curves of early childhood externalizing problems: differential susceptibility of children with difficult temperament. Journal of Abnormal Child Psychology 37(5): 625-636

Crispen, P.L.; Wong, Y.-N.; Greenberg, R.E.; Chen, D.Y.T.; Uzzo, R.G. 2008: Predicting growth of solid renal masses under active surveillance. Urologic Oncology 26(5): 555-559

Mejlholm, O.; Gunvig, A.; Borggaard, C.; Blom-Hanssen, J.; Mellefont, L.; Ross, T.; Leroi, F.; Else, T.; Visser, D.; Dalgaard, P. 2010: Predicting growth rates and growth boundary of Listeria monocytogenes - An international validation study with focus on processed and ready-to-eat meat and seafood. International Journal of Food Microbiology 141(3): 137-150

Salah, N.; Abd El Dayem, S.M.; Fawaz, L.; Ibrahim, M. 2013: Predicting growth response among Egyptian prepubertal idiopathic isolated growth hormone deficient children. Journal of Pediatric Endocrinology and Metabolism: Jpem 26(3-4): 247-255

Ingham, S.C.; Borneman, D.L.; Ané, C.éc.; Ingham, B.H. 2010: Predicting growth-no growth of Listeria monocytogenes on vacuum-packaged ready-to-eat meats. Journal of Food Protection 73(4): 708-714

Vishwakarma, R.K.; Shivhare, U.S.; Nanda, S.K. 2012: Predicting guar seed splitting by compression between two plates using Hertz theory of contact stresses. Journal of Food Science 77(9): E231-E239

Sinha, N.; Sen, S. 2011: Predicting hERG activities of compounds from their 3D structures: development and evaluation of a global descriptors based QSAR model. European Journal of Medicinal Chemistry 46(2): 618-630

Solhjouy-Fard, S.; Sarafrazi, A.; Minbashi Moeini, M.; Ahadiyat, A. 2013: Predicting habitat distribution of five heteropteran pest species in Iran. Journal of Insect Science 13: 116

Gogol-Prokurat, M. 2011: Predicting habitat suitability for rare plants at local spatial scales using a species distribution model. Ecological Applications: a Publication of the Ecological Society of America 21(1): 33-47

Laybourne, A.H.; Biggs, S.; Martin, F.C. 2011: Predicting habitual physical activity using coping strategies in older fallers engaged in falls-prevention exercise. Journal of Aging and Physical Activity 19(3): 189-200

Gijbels, D.; Alders, G.; Van Hoof, E.; Charlier, C.; Roelants, M.; Broekmans, T.; Eijnde, B.Op.'t.; Feys, P. 2010: Predicting habitual walking performance in multiple sclerosis: relevance of capacity and self-report measures. Multiple Sclerosis 16(5): 618-626

van Kuijk, A.A.; Pasman, J.W.; Hendricks, H.T.; Zwarts, M.J.; Geurts, A.C.H. 2009: Predicting hand motor recovery in severe stroke: the role of motor evoked potentials in relation to early clinical assessment. Neurorehabilitation and Neural Repair 23(1): 45-51

Bishop, A.J.; Martin, P.; MacDonald, M.; Poon, L.; Jazwinski, S.M.; Green, R.C.; Gearing, M.; Markesbery, W.R.; Woodard, J.L.; Johnson, M.A.; Tenover, J.S.; Siegler, I.C.; Rodgers, W.L.; Hausman, D.B.; Rott, C.; Davey, A.; Arnold, J. 2010: Predicting happiness among centenarians. Gerontology 56(1): 88-92

Kimmelman, J.; London, A.John. 2011: Predicting harms and benefits in translational trials: ethics, evidence, and uncertainty. Plos Medicine 8(3): E1001010

Sebanc, A.M.; Kearns, K.T.; Hernandez, M.D.; Galvin, K.B. 2007: Predicting having a best friend in young children: individual characteristics and friendship features. Journal of Genetic Psychology 168(1): 81-95

Scott, H.D.; Cabral, R.M. 1988: Predicting hazardous lifestyles among adolescents based on health-risk assessment data. American Journal of Health Promotion: Ajhp 2(4): 23-28

Weaver, A.S.; Zakrajsek, A.D.; Lewandowski, B.E.; Brooker, J.E.; Myers, J.G. 2013: Predicting head injury risk during International Space Station increments. Aviation Space and Environmental Medicine 84(1): 38-46

Anderson, L.R.; Mellor, J.M. 2008: Predicting health behaviors with an experimental measure of risk preference. Journal of Health Economics 27(5): 1260-1274

Varga, L.M.; Surratt, H.L. 2014: Predicting health care utilization in marginalized populations: Black, female, street-based sex workers. Women's Health Issues: Official Publication of the Jacobs Institute of Women's Health 24(3): E335-E343

Todd, L.; Hoffman-Goetz, L. 2011: Predicting health literacy among English-as-a-second-Language older Chinese immigrant women to Canada: comprehension of colon cancer prevention information. Journal of cancer education: the official journal of the American Association for Cancer Education 26(2): 326-332

Huppertz, J.W. 2008: Predicting health plan member retention from satisfaction surveys: the moderating role of intention and complaint voicing. Health Marketing Quarterly 25(4): 383-404

Mittmann, N.; Hitzig, S.L.; Catharine Craven, B. 2014: Predicting health preference in chronic spinal cord injury. Journal of Spinal Cord Medicine 37(5): 548-555

Morris, J.H.; van Wijck, F.; Joice, S.; Donaghy, M. 2013: Predicting health related quality of life 6 months after stroke: the role of anxiety and upper limb dysfunction. Disability and Rehabilitation 35(4): 291-299

Payakachat, N.; Tilford, J.M.; Kuhlthau, K.A.; van Exel, N.J.; Kovacs, E.; Bellando, J.; Pyne, J.M.; Brouwer, W.B.F. 2014: Predicting health utilities for children with autism spectrum disorders. Autism Research: Official Journal of the International Society for Autism Research 7(6): 649-663

Forslund, M.V.; Roe, C.; Sigurdardottir, S.; Andelic, N. 2013: Predicting health-related quality of life 2 years after moderate-to-severe traumatic brain injury. Acta Neurologica Scandinavica 128(4): 220-227

Wu, Y.P.; Steele, R.G. 2013: Predicting health-related quality of life from the psychosocial profiles of youth seeking treatment for obesity. Journal of Developmental and Behavioral Pediatrics: Jdbp 34(8): 575-582

Hatzmann, J.; Valstar, M.J.; Bosch, A.M.; Wijburg, F.A.; Heymans, H.S.A.; Grootenhuis, M.A. 2009: Predicting health-related quality of life of parents of children with inherited metabolic diseases. Acta Paediatrica 98(7): 1205-1210

Skolasky, R.L.; Carreon, L.Y.; Anderson, P.A.; Albert, T.J.; Riley, L.H. 2011: Predicting health-utility scores from the Cervical Spine Outcomes Questionnaire in a multicenter nationwide study of anterior cervical spine surgery. Spine 36(25): 2211-2216

Kaplan, J. 2007: Predicting health. A six step implementation process may help providers improve patient care, best practices and ROI. Healthcare Informatics: the Business Magazine for Information and Communication Systems 24(7): 55-56

Hendriks, H.; van den Putte, B.; de Bruijn, G-Jan.; de Vreese, C.H. 2014: Predicting health: the interplay between interpersonal communication and health campaigns. Journal of Health Communication 19(5): 625-636

Mohr, D.C.; VanDeusen Lukas, C.; Meterko, M. 2008: Predicting healthcare employees' participation in an office redesign program: attitudes, norms and behavioral control. Implementation Science: Is 3: 47

Kuo, R.N.; Dong, Y.-H.; Liu, J.-P.; Chang, C.-H.; Shau, W.-Y.; Lai, M.-S. 2011: Predicting healthcare utilization using a pharmacy-based metric with the WHO's Anatomic Therapeutic Chemical algorithm. Medical Care 49(11): 1031-1039

Södergren, M.; Wang, W.C.; Salmon, J.; Ball, K.; Crawford, D.; McNaughton, S.A. 2014: Predicting healthy lifestyle patterns among retirement age older adults in the WELL study: a latent class analysis of sex differences. Maturitas 77(1): 41-46

Scheib, C. 2009: Predicting healthy outcomes. Health Management Technology 30(5): 22-24

McCaslin, D.L. 2014: Predicting hearing aid outcomes. Journal of the American Academy of Audiology 25(2): 132

Darras, B.T.; Tawil, R. 2013: Predicting hearing loss in facioscapulohumeral muscular dystrophy. Neurology 81(16): 1370-1371

Smith, M.E. 2012: Predicting hearing loss in fishes. Advances in Experimental Medicine and Biology 730: 259-262

Hsu, R.-F.; Ho, C.-K.; Lu, S.-N.; Chen, S.-S. 2010: Predicting hearing thresholds and occupational hearing loss with multiple-frequency auditory steady-state responses. Journal of Otolaryngology - Head and Neck Surgery 39(5): 504-510

Attias, J.; Karawani, H.; Shemesh, R.; Nageris, B. 2014: Predicting hearing thresholds in occupational noise-induced hearing loss by auditory steady state responses. Ear and Hearing 35(3): 330-338

Anonymous 2014: Predicting heart disease risk in women. Biomarkers could give your doctor a window into your heart risks, so you can start making changes to reverse them. Harvard Women's Health Watch 21(12): 3

Acquatella, H. 2008: Predicting heart failure and mortality in chronic Chagas' heart disease. a novel disorder in Spain. Revista Espanola de Cardiologia 61(2): 105-107

Ristow, B.; Ali, S.; Na, B.; Turakhia, M.P.; Whooley, M.A.; Schiller, N.B. 2010: Predicting heart failure hospitalization and mortality by quantitative echocardiography: is body surface area the indexing method of choice? the Heart and Soul Study. Journal of the American Society of Echocardiography: Official Publication of the American Society of Echocardiography 23(4): 406-413

Tu, J.V. 2013: Predicting heart failure mortality from administrative data: can it be improved?. Canadian Journal of Cardiology 29(9): 1024-1026

Tjam, E.Y.; Heckman, G.A.; Smith, S.; Arai, B.; Hirdes, J.; Poss, J.; McKelvie, R.S. 2012: Predicting heart failure mortality in frail seniors: comparing the NYHA functional classification with the Resident Assessment Instrument (RAI) 2.0. International journal of cardiology 155(1): 75-80

Laing, C.; Jung, S.; Kim, N.; Elmetwaly, S.; Zahran, M.; Schlick, T. 2013: Predicting helical topologies in RNA junctions as tree graphs. Plos one 8(8): E71947

Bolboli Nojini, Z.; Abbas Rafati, A.; Majid Hashemianzadeh, S.; Samiee, S. 2011: Predicting helium and neon adsorption and separation on carbon nanotubes by Monte Carlo simulation. Journal of Molecular Modeling 17(4): 785-794

Apgar, J.R.; Gutwin, K.N.; Keating, A.E. 2008: Predicting helix orientation for coiled-coil dimers. Proteins 72(3): 1048-1065

Wu, C.; Wang, K.; Sun, T.; Xu, D.; Palmer, M.H. 2015: Predicting help-seeking intention of women with urinary incontinence in Jinan, China: a theory of planned behaviour model. Journal of Clinical Nursing 24(3-4): 457-464

Baechler, S.éb.; Hobbs, R.F.; Jacene, H.A.; Bochud, F.ço.O.; Wahl, R.L.; Sgouros, G. 2010: Predicting hematologic toxicity in patients undergoing radioimmunotherapy with 90Y-ibritumomab tiuxetan or 131I-tositumomab. Journal of Nuclear Medicine: Official Publication Society of Nuclear Medicine 51(12): 1878-1884

Brouwers, H.B.; Chang, Y.; Falcone, G.J.; Cai, X.; Ayres, A.M.; Battey, T.W.K.; Vashkevich, A.; McNamara, K.A.; Valant, V.; Schwab, K.; Orzell, S.C.; Bresette, L.M.; Feske, S.K.; Rost, N.S.; Romero, J.M.; Viswanathan, A.; Chou, S.H.-Y.; Greenberg, S.M.; Rosand, J.; Goldstein, J.N. 2014: Predicting hematoma expansion after primary intracerebral hemorrhage. JAMA Neurology 71(2): 158-164

Duong, H.K.; Bolwell, B.J.; Rybicki, L.; Koo, A.; Hsi, E.D.; Figueroa, P.; Dean, R.; Pohlman, B.; Kalaycio, M.; Andresen, S.; Sobecks, R.; Copelan, E. 2011: Predicting hematopoietic stem cell mobilization failure in patients with multiple myeloma: a simple method using day 1 CD34+ cell yield. Journal of Clinical Apheresis 26(3): 111-115

Salimi, J.; Razeghi, E.; Karjalian, H.; Meysamie, A.; Dahhaz, M.; Dadmehr, M. 2008: Predicting hemodialysis access failure with the measurement of dialysis access recirculation. Saudi Journal of Kidney Diseases and Transplantation: An Official Publication of the Saudi Center for Organ Transplantation Saudi Arabia 19(5): 781-784

Lacson, R. 2008: Predicting hemodialysis mortality utilizing blood pressure trends. AMIA .. Annual Symposium Proceedings. AMIA Symposium 2008: 369-373

Zheng, Q.; Fu, S.; Chen, D.; Li, X.; Li, Y.; Li, Y.; Yu, J.; Gong, M.; Bai, J. 2013: Predicting hemorrhage and obstruction in the elderly population using thromboelastographic indices. Clinical Interventions in Aging 8: 1405-1412

Zhang, H.-X.; Xu, X.-Q.; Fu, J.-F.; Lai, C.; Chen, X.-F. 2015: Predicting hepatic steatosis and liver fat content in obese children based on biochemical parameters and anthropometry. Pediatric Obesity 10(2): 112-117

Rehm, J.L.; Connor, E.L.; Wolfgram, P.M.; Eickhoff, J.C.; Reeder, S.B.; Allen, D.B. 2014: Predicting hepatic steatosis in a racially and ethnically diverse cohort of adolescent girls. Journal of Pediatrics 165(2): 319-325.E1

Zhang, Y.-J.; Wu, H.-C.; Shen, J.; Ahsan, H.; Tsai, W.Y.; Yang, H.-I.; Wang, L.-Y.; Chen, S.-Y.; Chen, C.-J.; Santella, R.M. 2007: Predicting hepatocellular carcinoma by detection of aberrant promoter methylation in serum DNA. Clinical Cancer Research: An Official Journal of the American Association for Cancer Research 13(8): 2378-2384

Brown, T.N. 2014: Predicting hexadecane-air equilibrium partition coefficients (L) using a group contribution approach constructed from high quality data. Sar and Qsar in Environmental Research 25(1): 51-71

Gardner, S.P.; Roberts-Thomson, K.F. 2012: Predicting high achievers in the University of Adelaide, Australia, Bachelor of Oral Health program, 2002-09. Journal of dental education 76(12): 1646-1656

Soubrane, O.; Brouquet, A.; Zalinski, S.ép.; Terris, B.ît.; Brézault, C.; Mallet, V.; Goldwasser, F.ço.; Scatton, O. 2010: Predicting high grade lesions of sinusoidal obstruction syndrome related to oxaliplatin-based chemotherapy for colorectal liver metastases: correlation with post-hepatectomy outcome. Annals of Surgery 251(3): 454-460

Green, D.L.; Berry, L.A.; Chen, G.; Ryan, P.M.; Canik, J.M.; Jaeger, E.F. 2011: Predicting high harmonic ion cyclotron heating efficiency in Tokamak plasmas. Physical Review Letters 107(14): 145001

Rutledge, D.N.; Jones, K.; Jones, C.J. 2007: Predicting high physical function in people with fibromyalgia. Journal of Nursing Scholarship: An Official Publication of Sigma Theta Tau International Honor Society of Nursing 39(4): 319-324

Murphy, C.R.; Hudson, L.O.; Spratt, B.G.; Quan, V.; Kim, D.; Peterson, E.; Tan, G.; Evans, K.; Meyers, H.; Cheung, M.; Lee, B.Y.; Mukamel, D.B.; Enright, M.C.; Whealon, M.; Huang, S.S. 2013: Predicting high prevalence of community methicillin-resistant Staphylococcus aureus strains in nursing homes. Infection Control and Hospital Epidemiology 34(3): 325-326

Clark, H.K.; Shamblen, S.R.; Ringwalt, C.L.; Hanley, S. 2012: Predicting high risk adolescents' substance use over time: the role of parental monitoring. Journal of Primary Prevention 33(2-3): 67-77

Vesprini, D.; Liu, S.; Nam, R. 2013: Predicting high risk disease using serum and DNA biomarkers. Current Opinion in Urology 23(3): 252-260

Olsson, G.E.; Hjertqvist, M.; Lundkvist, A.; Hörnfeldt, B. 2009: Predicting high risk for human hantavirus infections, Sweden. Emerging Infectious Diseases 15(1): 104-106

Hunt, M.K.; Hopko, D.R. 2009: Predicting high school truancy among students in the Appalachian south. Journal of Primary Prevention 30(5): 549-567

Lubin, A.S.; Snydman, D.R.; Ruthazer, R.; Bide, P.; Golan, Y. 2011: Predicting high vancomycin minimum inhibitory concentration in methicillin-resistant Staphylococcus aureus bloodstream infections. Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America 52(8): 997-1002

Chen, S.-Y.; Hsueh, P.-R.; Chiang, W.-C.; Huang, E.P.-C.; Lin, C.-F.; Chang, C.-H.; Chen, S.-C.; Chen, W.-J.; Chang, S.-C.; Lai, M.-S.; Chie, W.-C. 2014: Predicting high vancomycin minimum inhibitory concentration isolate infection among patients with community-onset methicillin-resistant Staphylococcus aureus bacteraemia. Journal of Infection 69(3): 259-265

Leininger, L.J.; Friedsam, D.; Voskuil, K.; DeLeire, T. 2014: Predicting high-need cases among new Medicaid enrollees. American Journal of Managed Care 20(9): E399-E407

Donovan, M.J.; Cordon-Cardo, C. 2013: Predicting high-risk disease using tissue biomarkers. Current Opinion in Urology 23(3): 245-251

Lamont, A.E.; Woodlief, D.; Malone, P.S. 2014: Predicting high-risk versus higher-risk substance use during late adolescence from early adolescent risk factors using Latent Class Analysis. Addiction Research and Theory 22(1): 78-89

Robertson, S.; Woods, C.; Gastin, P. 2015: Predicting higher selection in elite junior Australian Rules football: The influence of physical performance and anthropometric attributes. Journal of Science and Medicine in Sport 18(5): 601-606

Hsing, M.; Byler, K.; Cherkasov, A. 2009: Predicting highly-connected hubs in protein interaction networks by QSAR and biological data descriptors. Bioinformation 4(4): 164-168

Yun, H.; Delzell, E.; Ensrud, K.E.; Kilgore, M.L.; Becker, D.; Morrisey, M.A.; Curtis, J.R. 2010: Predicting hip and major osteoporotic fractures using administrative data. Archives of Internal Medicine 170(21): 1940-1942

Ebell, M.H. 2007: Predicting hip fracture risk in older women. American Family Physician 76(2): 273-275

Fulk, G.D.; Reynolds, C.; Mondal, S.; Deutsch, J.E. 2010: Predicting home and community walking activity in people with stroke. Archives of Physical Medicine and Rehabilitation 91(10): 1582-1586

Warren, J.R.; Okuyemi, K.S.; Guo, H.; Thomas, J.L.; Ahluwalia, J.S. 2010: Predicting home smoking restrictions among African American light smokers. American journal of health behavior 34(1): 110-118

Qiu, J.-D.; Sun, X.-Y.; Suo, S.-B.; Shi, S.-P.; Huang, S.-Y.; Liang, R.-P.; Zhang, L. 2011: Predicting homo-oligomers and hetero-oligomers by pseudo-amino acid composition: an approach from discrete wavelet transformation. Biochimie 93(7): 1132-1138

Bostan, B.; Greiner, R.; Szafron, D.; Lu, P. 2009: Predicting homologous signaling pathways using machine learning. Bioinformatics 25(22): 2913-2920

Poteat, V.P.; DiGiovanni, C.D.; Scheer, J.R. 2013: Predicting homophobic behavior among heterosexual youth: domain general and sexual orientation-specific factors at the individual and contextual level. Journal of Youth and Adolescence 42(3): 351-362

Cedergreen, N. 2010: Predicting hormesis in mixtures. Integrated Environmental Assessment and Management 6(2): 310-311

Ge, H.-L.; Liu, S.-S.; Zhu, X.-W.; Liu, H.-L.; Wang, L.-J. 2011: Predicting hormetic effects of ionic liquid mixtures on luciferase activity using the concentration addition model. Environmental Science and Technology 45(4): 1623-1629

Kozma, C.M.; Dirani, R.G.; Canuso, C.M.; Mao, L. 2010: Predicting hospital admission and discharge with symptom or function scores in patients with schizophrenia: pooled analysis of a clinical trial extension. Annals of General Psychiatry 9: 24

LaMantia, M.A.; Platts-Mills, T.F.; Biese, K.; Khandelwal, C.; Forbach, C.; Cairns, C.B.; Busby-Whitehead, J.; Kizer, J.S. 2010: Predicting hospital admission and returns to the emergency department for elderly patients. Academic Emergency Medicine: Official Journal of the Society for Academic Emergency Medicine 17(3): 252-259

Dexheimer, J.W.; Leegon, J.; Aronsky, D. 2007: Predicting hospital admission at triage in an emergency department. AMIA .. Annual Symposium Proceedings. AMIA Symposium 2007: 937

Fotheringham, J.; Caskey, F. 2014: Predicting hospital admissions by looking backwards: an alternative perspective. Nephrology Dialysis Transplantation: Official Publication of the European Dialysis and Transplant Association - European Renal Association 29(2): 225-227

Joseph, B.; Pandit, V.; Rhee, P.; Aziz, H.; Sadoun, M.; Wynne, J.; Tang, A.; Kulvatunyou, N.; O'Keeffe, T.; Fain, M.J.; Friese, R.S. 2014: Predicting hospital discharge disposition in geriatric trauma patients: is frailty the answer?. Journal of Trauma and Acute Care Surgery 76(1): 196-200

Ismail, Z.; Arenovich, T.; Grieve, C.; Willett, P.; Sajeev, G.; Mamo, D.C.; Macqueen, G.M.; Mulsant, B.H. 2012: Predicting hospital length of stay for geriatric patients with mood disorders. Canadian Journal of Psychiatry. Revue Canadienne de Psychiatrie 57(11): 696-703

Van den Bosch, W.F.; Kelder, J.C.; Wagner, C. 2011: Predicting hospital mortality among frequently readmitted patients: HSMR biased by readmission. Bmc Health Services Research 11: 57

Rahmanian, P.B.; Adams, D.H.; Castillo, J.G.; Carpentier, A.; Filsoufi, F. 2010: Predicting hospital mortality and analysis of long-term survival after major noncardiac complications in cardiac surgery patients. Annals of Thoracic Surgery 90(4): 1221-1229

Chang, C.J.; Tam, H.P.; Ko, W.J.; Tsai, P.R. 2013: Predicting hospital mortality in adult patients with prolonged stay (>14 days) in surgical intensive care unit. Minerva Anestesiologica 79(8): 843-852

Libório, A.B.; Abreu, K.L.ív.S.; Silva, G.B.; Lima, R.S.A.; Barreto, A.G.C.; Barbosa, O.A.; Daher, E.F. 2011: Predicting hospital mortality in critically ill cancer patients according to acute kidney injury severity. Oncology 80(3-4): 160-166

Su, Y.-Y.; Li, X.; Li, S.-j.; Luo, R.; Ding, J.-p.; Wang, L.; Cao, G.-h.; Wang, D.-y.; Gao, J.-x. 2009: Predicting hospital mortality using APACHE Ii scores in neurocritically ill patients: a prospective study. Journal of Neurology 256(9): 1427-1433

Clark, D.E.; Lucas, F.L.; Ryan, L.M. 2007: Predicting hospital mortality, length of stay, and transfer to long-term care for injured patients. Journal of Trauma 62(3): 592-600

Kiefe, C.; Allison, J.J.; de Lissovoy, G. 2013: Predicting hospital readmission: different approaches raise new questions about old issues. Medical Care 51(1): 11-12

Hupert, N.; Wattson, D.; Cuomo, J.; Hollingsworth, E.; Neukermans, K.; Xiong, W. 2009: Predicting hospital surge after a large-scale anthrax attack: a model-based analysis of CDC's cities readiness initiative prophylaxis recommendations. Medical Decision Making: An International Journal of the Society for Medical Decision Making 29(4): 424-437

Drawz, P.E.; Miller, R.Tyler.; Sehgal, A.R. 2008: Predicting hospital-acquired acute kidney injury--a case-controlled study. Renal Failure 30(9): 848-855

Chang, Y.-J.; Yeh, M.-L.; Li, Y.-C.; Hsu, C.-Y.; Lin, C.-C.; Hsu, M.-S.; Chiu, W.-T. 2011: Predicting hospital-acquired infections by scoring system with simple parameters. Plos one 6(8): E23137

Zhang, J.; Goode, K.M.; Cuddihy, P.E.; Cleland, J.G.F. 2009: Predicting hospitalization due to worsening heart failure using daily weight measurement: analysis of the Trans-European Network-Home-Care Management System (TEN-HMS) study. European Journal of Heart Failure 11(4): 420-427

Buyuktiryaki, A.B.; Civelek, E.; Can, D.; Orhan, F.ıl.; Aydogan, M.; Reisli, I.; Keskin, O.; Akcay, A.; Yazicioglu, M.; Cokugras, H.; Yuksel, H.; Zeyrek, D.; Kocak, A.K.; Sekerel, B.E. 2013: Predicting hospitalization in children with acute asthma. Journal of Emergency Medicine 44(5): 919-927

Goldberg, J.F.; Ernst, C.L.; Bird, S. 2007: Predicting hospitalization versus discharge of suicidal patients presenting to a psychiatric emergency service. Psychiatric Services 58(4): 561-565

Fuller, T.L.; Gilbert, M.; Martin, V.; Cappelle, J.; Hosseini, P.; Njabo, K.Y.; Abdel Aziz, S.; Xiao, X.; Daszak, P.; Smith, T.B. 2013: Predicting hotspots for influenza virus reassortment. Emerging Infectious Diseases 19(4): 581-588

Arhami, M.; Kamali, N.; Rajabi, M.Mahdi. 2013: Predicting hourly air pollutant levels using artificial neural networks coupled with uncertainty analysis by Monte Carlo simulations. Environmental Science and Pollution Research International 20(7): 4777-4789

Dong, B.; Zhang, P.; Chen, X.; Liu, L.; Wang, Y.; He, S.; Chen, R. 2011: Predicting housekeeping genes based on Fourier analysis. Plos one 6(6): E21012

Brockerhoff, E.G.; Kimberley, M.; Liebhold, A.M.; Haack, R.A.; Cavey, J.F. 2014: Predicting how altering propagule pressure changes establishment rates of biological invaders across species pools. Ecology 95(3): 594-601

Kim, D.-H.; Wirtz, D. 2013: Predicting how cells spread and migrate: focal adhesion size does matter. Cell Adhesion and Migration 7(3): 293-296

Ingebrigtsen, T.S.; Errington, J.R.; Truskett, T.M.; Dyre, J.C. 2013: Predicting how nanoconfinement changes the relaxation time of a supercooled liquid. Physical Review Letters 111(23): 235901

Perron, N.R.; Hodges, J.N.; Jenkins, M.; Brumaghim, J.L. 2008: Predicting how polyphenol antioxidants prevent DNA damage by binding to iron. Inorganic Chemistry 47(14): 6153-6161

Collen, B.; McRae, L.; Deinet, S.; De Palma, A.; Carranza, T.; Cooper, N.; Loh, J.; Baillie, J.E.M. 2011: Predicting how populations decline to extinction. Philosophical Transactions of the Royal Society of London. Series B Biological Sciences 366(1577): 2577-2586

Kilpatrick, A.M.; Pape, W.J. 2013: Predicting human West Nile virus infections with mosquito surveillance data. American Journal of Epidemiology 178(5): 829-835

Ou, X.-l.; Gao, J.; Wang, H.; Wang, H.-s.; Lu, H.-l.; Sun, H.-y. 2012: Predicting human age with bloodstains by sjTREC quantification. Plos one 7(8): E42412

West, P.R.; Weir, A.M.; Smith, A.M.; Donley, E.L.R.; Cezar, G.G. 2010: Predicting human developmental toxicity of pharmaceuticals using human embryonic stem cells and metabolomics. Toxicology and Applied Pharmacology 247(1): 18-27

Brison, D.R.; Hollywood, K.; Arnesen, R.; Goodacre, R. 2007: Predicting human embryo viability: the road to non-invasive analysis of the secretome using metabolic footprinting. Reproductive Biomedicine Online 15(3): 296-302

Malmborg, J.; Ploeger, B.A. 2013: Predicting human exposure of active drug after oral prodrug administration, using a joined in vitro/in silico-in vivo extrapolation and physiologically-based pharmacokinetic modeling approach. Journal of Pharmacological and Toxicological Methods 67(3): 203-213

Xu, J.; Jiang, M.; Wang, S.; Kankanhalli, M.S.; Zhao, Q. 2014: Predicting human gaze beyond pixels. Journal of Vision 14(1)

Borchani, H.; Bielza, C.; Toro, C.; Larrañaga, P. 2013: Predicting human immunodeficiency virus inhibitors using multi-dimensional Bayesian network classifiers. Artificial Intelligence in Medicine 57(3): 219-229

Li, X.; Hu, H.; Shu, L. 2010: Predicting human immunodeficiency virus protease cleavage sites in nonlinear projection space. Molecular and Cellular Biochemistry 339(1-2): 127-133

Zakeri-Milani, P.; Valizadeh, H.; Tajerzadeh, H.; Azarmi, Y.; Islambolchilar, Z.; Barzegar, S.; Barzegar-Jalali, M. 2007: Predicting human intestinal permeability using single-pass intestinal perfusion in rat. Journal of Pharmacy and Pharmaceutical Sciences: a Publication of the Canadian Society for Pharmaceutical Sciences Societe Canadienne des Sciences Pharmaceutiques 10(3): 368-379

Jiang, Q.; Wang, G.; Jin, S.; Li, Y.; Wang, Y. 2013: Predicting human microRNA-disease associations based on support vector machine. International Journal of Data Mining and Bioinformatics 8(3): 282-293

He, B.; Bai, J.; Zipunnikov, V.V.; Koster, A.; Caserotti, P.; Lange-Maia, B.; Glynn, N.W.; Harris, T.B.; Crainiceanu, C.M. 2014: Predicting human movement with multiple accelerometers using movelets. Medicine and Science in Sports and Exercise 46(9): 1859-1866

Revell, V.L.; Barrett, D.C.G.; Schlangen, L.J.M.; Skene, D.J. 2010: Predicting human nocturnal nonvisual responses to monochromatic and polychromatic light with a melanopsin photosensitivity function. Chronobiology International 27(9-10): 1762-1777

Gupta, S.; Dennis, J.; Thurman, R.E.; Kingston, R.; Stamatoyannopoulos, J.A.; Noble, W.S. 2008: Predicting human nucleosome occupancy from primary sequence. Plos Computational Biology 4(8): E1000134

Bowyer, H.L.; Forster, A.S.; Marlow, L.A.V.; Waller, J. 2014: Predicting human papillomavirus vaccination behaviour among adolescent girls in England: results from a prospective survey. Journal of Family Planning and Reproductive Health Care 40(1): 14-22

Katz, M.L.; Kam, J.A.; Krieger, J.L.; Roberto, A.J. 2012: Predicting human papillomavirus vaccine intentions of college-aged males: an examination of parents' and son's perceptions. Journal of American college health: J of ACH 60(6): 449-459

Guimerà, R.; Llorente, A.; Moro, E.; Sales-Pardo, M. 2012: Predicting human preferences using the block structure of complex social networks. Plos one 7(9): E44620

Du, P.; Wang, L. 2014: Predicting human protein subcellular locations by the ensemble of multiple predictors via protein-protein interaction network with edge clustering coefficients. PloS one 9(1): e86879

Honey, C.J.; Sporns, O.; Cammoun, L.; Gigandet, X.; Thiran, J.P.; Meuli, R.; Hagmann, P. 2009: Predicting human resting-state functional connectivity from structural connectivity. Proceedings of the National Academy of Sciences of the United States of America 106(6): 2035-2040

Lu, Y.; Rajaraman, S.; Ward, W.K.; Vigersky, R.A.; Reifman, J. 2011: Predicting human subcutaneous glucose concentration in real time: a universal data-driven approach. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 2011: 7945-7948

Zhou, Q.; Guo, P.; Kruh, G.D.; Vicini, P.; Wang, X.; Gallo, J.M. 2007: Predicting human tumor drug concentrations from a preclinical pharmacokinetic model of temozolomide brain disposition. Clinical Cancer Research: An Official Journal of the American Association for Cancer Research 13(14): 4271-4279

Johnson, L.; Sullivan, B.; Hayhoe, M.; Ballard, D. 2014: Predicting human visuomotor behaviour in a driving task. Philosophical Transactions of the Royal Society of London. Series B Biological Sciences 369(1636): 20130044

Martin, A.E.; Schmiedeler, J.P. 2014: Predicting human walking gaits with a simple planar model. Journal of Biomechanics 47(6): 1416-1421

Xu, S.; Zhu, D.; Zhang, Q. 2014: Predicting hybrid performance in rice using genomic best linear unbiased prediction. Proceedings of the National Academy of Sciences of the United States of America 111(34): 12456-12461

Garrido, N.M.; Jorge, M.; Queimada, A.ón.J.; Gomes, J.é R.B.; Economou, I.G.; Macedo, E.én.A. 2011: Predicting hydration Gibbs energies of alkyl-aromatics using molecular simulation: a comparison of current force fields and the development of a new parameter set for accurate solvation data. Physical Chemistry Chemical Physics: Pccp 13(38): 17384-17394

Andersson, M.P.; Stipp, S.L.S. 2014: Predicting hydration energies for multivalent ions. Journal of Computational Chemistry 35(28): 2070-2075

Sulea, T.; Purisima, E.O. 2012: Predicting hydration free energies of polychlorinated aromatic compounds from the SAMPL-3 data set with FiSH and LIE models. Journal of Computer-Aided Molecular Design 26(5): 661-667

Klimovich, P.V.; Mobley, D.L. 2010: Predicting hydration free energies using all-atom molecular dynamics simulations and multiple starting conformations. Journal of Computer-Aided Molecular Design 24(4): 307-316

König, G.; Pickard, F.C.; Mei, Y.; Brooks, B.R. 2014: Predicting hydration free energies with a hybrid QM/MM approach: an evaluation of implicit and explicit solvation models in SAMPL4. Journal of Computer-Aided Molecular Design 28(3): 245-257

Sandberg, L. 2014: Predicting hydration free energies with chemical accuracy: the SAMPL4 challenge. Journal of Computer-Aided Molecular Design 28(3): 211-219

Gilli, P.; Pretto, L.; Bertolasi, V.; Gilli, G. 2009: Predicting hydrogen-bond strengths from acid-base molecular properties. the pK(a) slide rule: toward the solution of a long-lasting problem. Accounts of Chemical Research 42(1): 33-44

Adelstein, E.; Schwartzman, D.; Gorcsan, J.; Saba, S. 2011: Predicting hyperresponse among pacemaker-dependent nonischemic cardiomyopathy patients upgraded to cardiac resynchronization. Journal of Cardiovascular Electrophysiology 22(8): 905-911

Heagerty, A.M. 2007: Predicting hypertension complications from small artery structure. Journal of Hypertension 25(5): 939-940

Freire, A.ía.V.ón.; Ropelato, M.ía.G.; Ballerini, M.ía.G.; Acha, O.; Bergadá, I.; de Papendieck, L.G.ñe.; Chiesa, A. 2014: Predicting hypocalcemia after thyroidectomy in children. Surgery 156(1): 130-136

Lin, C.-S.; Chiu, J.-S.; Hsieh, M.-H.; Mok, M.S.; Li, Y.-C.; Chiu, H.-W. 2008: Predicting hypotensive episodes during spinal anesthesia with the application of artificial neural networks. Computer Methods and Programs in Biomedicine 92(2): 193-197

Giocos, G.; Kagee, A.; Swartz, L. 2008: Predicting hypothetical willingness to participate (WTP) in a future phase IIi HIV vaccine trial among high-risk adolescents. Aids and Behavior 12(6): 842-851

Petroff, A.B.; Martz, D.M.; Webb, R.M.; Galloway, A.T. 2011: Predicting ideal body mass index: what does clothing size have to do with it?. Body Image 8(2): 126-134

Neal, C.J.; McClendon, J.; Halpin, R.; Acosta, F.L.; Koski, T.; Ondra, S.L. 2011: Predicting ideal spinopelvic balance in adult spinal deformity. Journal of Neurosurgery. Spine 15(1): 82-91

Chen, M.; Borlak, J.ür.; Tong, W. 2014: Predicting idiosyncratic drug-induced liver injury: some recent advances. Expert Review of Gastroenterology and Hepatology 8(7): 721-723

Skidmore, J.R.; Dyson, S.J.; Kupper, A.E.; Calabrese, D. 2014: Predicting illness behavior: health anxiety mediated by locus of control. American Journal of Health Behavior 38(5): 699-707

Heikkinen, J.; Biancari, F.; Satta, J.; Salmela, E.; Mosorin, M.; Juvonen, T.; Lepojärvi, M. 2007: Predicting immediate and late outcome after surgery for mitral valve regurgitation with EuroSCORE. Journal of Heart Valve Disease 16(2): 116-121

Thong, T.; Raitt, M.H. 2007: Predicting imminent episodes of ventricular tachyarrhythmia using heart rate. Pacing and Clinical Electrophysiology: Pace 30(7): 874-884

Thong, T. 2008: Predicting imminent episodes of ventricular tachyarrhythmia--retrospective analysis of short R-R records from ICD. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 2008: 5664-5667

Parker, B.M.; Cywinski, J.B.; Alster, J.M.; Irefin, S.A.; Popovich, M.; Beven, M.; Fung, J.J. 2008: Predicting immunosuppressant dosing in the early postoperative period with noninvasive indocyanine green elimination following orthotopic liver transplantation. Liver Transplantation: Official Publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society 14(1): 46-52

Bratt, J.H. 2010: Predicting impact of price increases on demand for reproductive health services: can it be done well?. Health Policy 95(2-3): 159-165

Mathieu, D.; Alaime, T. 2014: Predicting impact sensitivities of nitro compounds on the basis of a semi-empirical rate constant. Journal of Physical Chemistry. a 118(41): 9720-9726

Fox, N.J.; White, P.C.L.; McClean, C.J.; Marion, G.; Evans, A.; Hutchings, M.R. 2011: Predicting impacts of climate change on Fasciola hepatica risk. Plos one 6(1): E16126

Ma, J.; Hu, Y.; Bu, R.; Chang, Y.; Deng, H.; Qin, Q. 2014: Predicting impacts of climate change on the aboveground carbon sequestration rate of a temperate forest in northeastern China. Plos one 9(4): E96157

Wu, Y.; Liu, S.; Gallant, A.L. 2012: Predicting impacts of increased CO₂ and climate change on the water cycle and water quality in the semiarid James River Basin of the Midwestern USA. Science of the Total Environment 430: 150-160

Seppänen, J.; Heinävaara, S.; Hakulinen, T. 2008: Predicting impacts of mass-screening policy changes on breast cancer mortality. Statistics in Medicine 27(25): 5235-5251

Nalloor, R.; Bunting, K.; Vazdarjanova, A. 2011: Predicting impaired extinction of traumatic memory and elevated startle. Plos one 6(5): E19760

Barkley, R.A.; Fischer, M. 2011: Predicting impairment in major life activities and occupational functioning in hyperactive children as adults: self-reported executive function (EF) deficits versus EF tests. Developmental Neuropsychology 36(2): 137-161

Leung, S.K.F.; Lam, F.M.; Yew, W.W.; Wong, P.C. 2008: Predicting impairment of diffusing capacity for Chinese patients with lung fibrosis. Respirology 13(6): 929-930

Macdonald, S.W.S.; Hultsch, D.F.; Dixon, R.A. 2008: Predicting impending death: inconsistency in speed is a selective and early marker. Psychology and Aging 23(3): 595-607

Torii, R.; Kalantzi, M.; Theodoropoulos, S.; Sarathchandra, P.; Xu, X.Y.; Yacoub, M.H. 2013: Predicting impending rupture of the ascending aorta with bicuspid aortic valve: spatiotemporal flow and wall shear stress. JACC. Cardiovascular Imaging 6(9): 1017-1019

Haliloglu, T.; Gul, A.; Erman, B. 2010: Predicting important residues and interaction pathways in proteins using Gaussian Network Model: binding and stability of HLA proteins. Plos Computational Biology 6(7): E1000845

Feldman, S.R.; Winkelman, W.; Baum, E.; Preston, N. 2013: Predicting improvement in signs and symptoms of plaque psoriasis after 1 week of treatment with clobetasol propionate 0.05% spray. Journal of Drugs in Dermatology: Jdd 12(12): 1456-1460

Kwakkel, G.; Kollen, B. 2007: Predicting improvement in the upper paretic limb after stroke: a longitudinal prospective study. Restorative Neurology and Neuroscience 25(5-6): 453-460

Westra, B.L.; Savik, K.; Oancea, C.; Choromanski, L.; Holmes, J.H.; Bliss, D. 2011: Predicting improvement in urinary and bowel incontinence for home health patients using electronic health record data. Journal of Wound Ostomy and Continence Nursing: Official Publication of Wound Ostomy and Continence Nurses Society 38(1): 77-87

Nieuwenhuijsen, K.; Cornelius, L.R.; de Boer, M.R.; Groothoff, J.W.; Frings-Dresen, M.H.W.; van der Klink, J.J.L.; Brouwer, S. 2014: Predicting improvement of functioning in disability claimants. Journal of Occupational Rehabilitation 24(3): 410-418

Haber, M.G.; Karpur, A.; Deschênes, N.; Clark, H.B. 2008: Predicting improvement of transitioning young people in the partnerships for youth transition initiative: findings from a multisite demonstration. Journal of Behavioral Health Services and Research 35(4): 488-513

Black, E.B.; Mildred, H. 2013: Predicting impulsive self-injurious behavior in a sample of adult women. Journal of Nervous and Mental Disease 201(1): 72-75

Kim, K.C.; Allendorf, M.D.; Stavila, V.; Sholl, D.S. 2010: Predicting impurity gases and phases during hydrogen evolution from complex metal hydrides using free energy minimization enabled by first-principles calculations. Physical Chemistry Chemical Physics: Pccp 12(33): 9918-9926

Nielsen, E.I.; Cars, O.; Friberg, L.E. 2011: Predicting in vitro antibacterial efficacy across experimental designs with a semimechanistic pharmacokinetic-pharmacodynamic model. Antimicrobial Agents and ChemoTherapy 55(4): 1571-1579

Riddick, G.; Song, H.; Ahn, S.; Walling, J.; Borges-Rivera, D.; Zhang, W.; Fine, H.A. 2011: Predicting in vitro drug sensitivity using Random Forests. Bioinformatics 27(2): 220-224

Heigoldt, U.; Sommer, F.; Daniels, R.; Wagner, K.-G. 2010: Predicting in vivo absorption behavior of oral modified release dosage forms containing pH-dependent poorly soluble drugs using a novel pH-adjusted biphasic in vitro dissolution test. European Journal of Pharmaceutics and Biopharmaceutics: Official Journal of Arbeitsgemeinschaft für Pharmazeutische Verfahrenstechnik E.V 76(1): 105-111

Zheng, B.; Tan, L.; Mo, X.; Yu, W.; Wang, Y.; Tucker-Kellogg, L.; Welsch, R.E.; So, P.T.C.; Yu, H. 2011: Predicting in vivo anti-hepatofibrotic drug efficacy based on in vitro high-content analysis. Plos one 6(11): E26230

Li, X.; Quon, G.; Lipshitz, H.D.; Morris, Q. 2010: Predicting in vivo binding sites of RNA-binding proteins using mRNA secondary structure. Rna 16(6): 1096-1107

Baker, J.G.; Kemp, P.; March, J.; Fretwell, L.; Hill, S.J.; Gardiner, S.M. 2011: Predicting in vivo cardiovascular properties of β-blockers from cellular assays: a quantitative comparison of cellular and cardiovascular pharmacological responses. Faseb Journal: Official Publication of the Federation of American Societies for Experimental Biology 25(12): 4486-4497

Smaldone, G.; Solomita, M. 2009: Predicting in vivo deposition in vitro. Journal of Aerosol Medicine and Pulmonary Drug Delivery 22(1): 9-10

Cunha, B.A. 2012: Predicting in vivo effectiveness from in vitro susceptibility: a step closer to performing testing of uropathogens in human urine. Scandinavian Journal of Infectious Diseases 44(9): 714-715

Epstein, H.; Rabinovich, L.; Banai, S.; Elazar, V.; Gao, J.; Chorny, M.; Danenebrg, H.D.; Golomb, G. 2008: Predicting in vivo efficacy of potential restenosis therapies by cell culture studies: species-dependent susceptibility of vascular smooth muscle cells. Open Cardiovascular Medicine Journal 2: 60-69

Henry, M.; Lavigne, R.; Debarbieux, L. 2013: Predicting in vivo efficacy of therapeutic bacteriophages used to treat pulmonary infections. Antimicrobial Agents and ChemoTherapy 57(12): 5961-5968

Stojanac, I.; Drobac, M.; Petrovic, L.; Atanackovic, T. 2012: Predicting in vivo failure of rotary nickel-titanium endodontic instruments under cyclic fatigue. Dental Materials Journal 31(4): 650-655

Akers, W.J.; Berezin, M.Y.; Lee, H.; Achilefu, S. 2008: Predicting in vivo fluorescence lifetime behavior of near-infrared fluorescent contrast agents using in vitro measurements. Journal of Biomedical Optics 13(5): 054042

Péry, A.R.R.; Brochot, C.él.; Desmots, S.; Boize, M.; Sparfel, L.; Fardel, O. 2011: Predicting in vivo gene expression in macrophages after exposure to benzo(a)pyrene based on in vitro assays and toxicokinetic/toxicodynamic models. Toxicology Letters 201(1): 8-14

Wolf, M.T.; Vodovotz, Y.; Tottey, S.; Brown, B.N.; Badylak, S.F. 2015: Predicting in vivo responses to biomaterials via combined in vitro and in silico analysis. Tissue Engineering. Part C Methods 21(2): 148-159

Greene, N.; Song, M. 2011: Predicting in vivo safety characteristics using physiochemical properties and in vitro assays. Future Medicinal Chemistry 3(12): 1503-1511

Doucet, F.J.; White, G.A.; Wulfert, F.; Hill, S.E.; Wiseman, J. 2010: Predicting in vivo starch digestibility coefficients in newly weaned piglets from in vitro assessment of diets using multivariate analysis. British Journal of Nutrition 103(9): 1309-1318

Alghnam, S.; Palta, M.; Hamedani, A.; Alkelya, M.; Remington, P.L.; Durkin, M.S. 2014: Predicting in-hospital death among patients injured in traffic crashes in Saudi Arabia. Injury 45(11): 1693-1699

Ndour, C.; Dossou Gbété, S.; Bru, N.; Abrahamowicz, M.; Fauconnier, A.; Traoré, M.; Diop, A.; Fournier, P.; Dumont, A. 2013: Predicting in-hospital maternal mortality in Senegal and Mali. Plos one 8(5): E64157

Tolenaar, J.L.; Froehlich, W.; Jonker, F.H.W.; Upchurch, G.R.; Rampoldi, V.; Tsai, T.T.; Bossone, E.; Evangelista, A.; O'Gara, P.; Pape, L.; Montgomery, D.; Isselbacher, E.M.; Nienaber, C.A.; Eagle, K.A.; Trimarchi, S. 2014: Predicting in-hospital mortality in acute type B aortic dissection: evidence from International Registry of Acute Aortic Dissection. Circulation 130(11 Suppl. 1): S45-S50

Grendar, J.; Shaheen, A.A.; Myers, R.P.; Parker, R.; Vollmer, C.M.; Ball, C.G.; Quan, M.L.; Kaplan, G.G.; Al-Manasra, T.; Dixon, E. 2012: Predicting in-hospital mortality in patients undergoing complex gastrointestinal surgery: determining the optimal risk adjustment method. Archives of Surgery 147(2): 126-135

Myers, R.P.; Quan, H.; Hubbard, J.N.; Shaheen, A.A.M.; Kaplan, G.G. 2009: Predicting in-hospital mortality in patients with cirrhosis: results differ across risk adjustment methods. Hepatology 49(2): 568-577

Ribera, A.; Marsal, J.R.; Ferreira-González, I.; Cascant, P.ó; Pons, J.M.V.; Mitjavila, F.; Salas, T.; Permanyer-Miralda, G.à 2008: Predicting in-hospital mortality with coronary bypass surgery using hospital discharge data: comparison with a prospective observational study. Revista Espanola de Cardiologia 61(8): 843-852

Marschollek, M.; Nemitz, G.; Gietzelt, M.; Wolf, K.H.; Meyer Zu Schwabedissen, H.; Haux, R. 2009: Predicting in-patient falls in a geriatric clinic: a clinical study combining assessment data and simple sensory gait measurements. Zeitschrift für Gerontologie und Geriatrie 42(4): 317-321

Xu, M.; Yu, L.; Wan, B.; Yu, L.; Huang, Q. 2011: Predicting inactive conformations of protein kinases using active structures: conformational selection of type-II inhibitors. Plos one 6(7): E22644

Allen, S.; Yeung, P.; Janczewski, M.; Siddique, N. 2008: Predicting inadequate spirometry technique and the use of FEV1/FEV3 as an alternative to FEV1/FVC for patients with mild cognitive impairment. Clinical Respiratory Journal 2(4): 208-213

Thomas, T.L.; Nandram, B. 2010: Predicting incidence and asymptomatic rates for chlamydia in small domains. Journal of Advanced Nursing 66(12): 2650-2658

Cheng, S.; Wang, T.J. 2009: Predicting incident CKD. American Journal of Kidney Diseases: the Official Journal of the National Kidney Foundation 53(6): 936-939

Marcus, G.M. 2010: Predicting incident atrial fibrillation: an important step toward primary prevention. Archives of Internal Medicine 170(21): 1874-1875

Cruz-Cabeza, A.J.; Day, G.M.; Jones, W. 2009: Predicting inclusion behaviour and framework structures in organic crystals. Chemistry 15(47): 13033-13040

Golino, H.Fernandes.; Amaral, L.Souza.de.Brito.; Duarte, S.Fernando.Pimentel.; Gomes, C.Mauro.Assis.; Soares, T.de.Jesus.; Dos Reis, L.Araujo.; Santos, J. 2014: Predicting increased blood pressure using machine learning. Journal of Obesity 2014: 637635

Denniss, A.Robert. 2013: Predicting increased risk of aortic dissection. Heart Lung and Circulation 22(1): 1-2

Jarošík, Věch.; Pyšek, P.; Foxcroft, L.C.; Richardson, D.M.; Rouget, M.; MacFadyen, S. 2011: Predicting incursion of plant invaders into Kruger National Park, South Africa: the interplay of general drivers and species-specific factors. Plos one 6(12): E28711

Corley, C.D.; Mihalcea, R.; Mikler, A.R.; Sanfilippo, A.P. 2011: Predicting individual affect of health interventions to reduce HPV prevalence. Advances in Experimental Medicine and Biology 696: 181-190

Bekker, M.H.J.; Croon, M.A.; van Balkom, E.G.A.; Vermee, J.B.G. 2008: Predicting individual differences in autonomy-connectedness: the role of body awareness, alexithymia, and assertiveness. Journal of Clinical Psychology 64(6): 747-765

Connors, B.L.; Rende, R.; Colton, T.J. 2013: Predicting individual differences in decision-making process from signature movement styles: an illustrative study of leaders. Frontiers in Psychology 4: 658

Nagata, K. 2008: Predicting individual differences in drug-metabolizing enzymes. Drug Metabolism and Pharmacokinetics 23(1): 1

Wager, T.D.; Atlas, L.Y.; Leotti, L.A.; Rilling, J.K. 2011: Predicting individual differences in placebo analgesia: contributions of brain activity during anticipation and pain experience. Journal of Neuroscience: the Official Journal of the Society for Neuroscience 31(2): 439-452

Harlaar, N.; Cutting, L.; Deater-Deckard, K.; Dethorne, L.S.; Justice, L.M.; Schatschneider, C.; Thompson, L.A.; Petrill, S.A. 2010: Predicting individual differences in reading comprehension: a twin study. Annals of Dyslexia 60(2): 265-288

Chandler, J.F.; Arnold, R.D.; Phillips, J.B.; Turnmire, A.E. 2013: Predicting individual differences in response to sleep loss: application of current techniques. Aviation Space and Environmental Medicine 84(9): 927-937

Zaak, D.; Burger, M.; Otto, W.; Bastian, P.J.; Denzinger, S.; Stief, C.G.; Buchner, H.; Hartmann, A.; Wieland, W.F.; Shariat, S.F.; Fritsche, H.-M. 2010: Predicting individual outcomes after radical cystectomy: an external validation of current nomograms. Bju International 106(3): 342-348

Chen, J.; Zhao, G. 2011: Predicting individual prognosis for patients undergoing resection of colorectal liver metastases. Annals of Surgical Oncology 18(3): 894-895

Van de Pas, N.C.A.; Rullmann, J.A.C.; Woutersen, R.A.; van Ommen, B.; Rietjens, I.M.C.M.; de Graaf, A.A. 2014: Predicting individual responses to pravastatin using a physiologically based kinetic model for plasma cholesterol concentrations. Journal of Pharmacokinetics and Pharmacodynamics 41(4): 351-362

Coderch, J.; Sánchez-Pérez, I.; Ibern, P.; Carreras, M.; Pérez-Berruezo, X.; Inoriza, J.é M. 2014: Predicting individual risk of high healthcare cost to identify complex chronic patients. Gaceta Sanitaria 28(4): 292-300

Lyman, G.H.; Kuderer, N.M.; Crawford, J.; Wolff, D.A.; Culakova, E.; Poniewierski, M.S.; Dale, D.C. 2011: Predicting individual risk of neutropenic complications in patients receiving cancer chemotherapy. Cancer 117(9): 1917-1927

Souto, J.C.; Soria, J.é M. 2012: Predicting individual risk of venous thrombosis. Blood 120(3): 500-501

Lagarde, S.M.; Reitsma, J.B.; Ten Kate, F.J.W.; Busch, O.R.C.; Obertop, H.; Zwinderman, A.H.; Moons, J.; van Lanschot, J.J.B.; Lerut, T. 2008: Predicting individual survival after potentially curative esophagectomy for adenocarcinoma of the esophagus or gastroesophageal junction. Annals of Surgery 248(6): 1006-1013

Cristia, A.; Seidl, A.; Junge, C.; Soderstrom, M.; Hagoort, P. 2014: Predicting individual variation in language from infant speech perception measures. Child Development 85(4): 1330-1345

Vo, L.T.K.; Walther, D.B.; Kramer, A.F.; Erickson, K.I.; Boot, W.R.; Voss, M.W.; Prakash, R.S.; Lee, H.; Fabiani, M.; Gratton, G.; Simons, D.J.; Sutton, B.P.; Wang, M.Y. 2011: Predicting individuals' learning success from patterns of pre-learning MRi activity. Plos one 6(1): E16093

Quinn, A.; Tamerius, J.D.; Perzanowski, M.; Jacobson, J.S.; Goldstein, I.; Acosta, L.; Shaman, J. 2014: Predicting indoor heat exposure risk during extreme heat events. Science of the Total Environment 490: 686-693

Martinez-Serrano, J.J.; Diez de Los Rios, A. 2014: Predicting induced activity in the Havar foils of the (18)F production targets of a PET cyclotron and derived radiological risk. Health Physics 107(2): 103-110

Sheu, R.J.; Jiang, S.H. 2010: Predicting induced radioactivity for the accelerator operations at the Taiwan Photon Source. Health Physics 99(6): 788-799

O'Keeffe, G.W.; Kenny, L.C. 2014: Predicting infant neurodevelopmental outcomes using the placenta?. Trends in Molecular Medicine 20(6): 303-305

Carrera, E.; Jones, P.S.; Alawneh, J.A.; Klærke Mikkelsen, I.; Cho, T.-H.; Siemonsen, S.; Guadagno, J.V.; Mouridsen, K.; Ribe, L.; Hjort, N.; Fryer, T.D.; Carpenter, T.A.; Aigbirhio, F.I.; Fiehler, J.; Nighoghossian, N.; Warburton, E.A.; Ostergaard, L.; Baron, J.-C. 2011: Predicting infarction within the diffusion-weighted imaging lesion: does the mean transit time have added value?. Stroke 42(6): 1602-1607

Schley, D.; Burgin, L.; Gloster, J. 2009: Predicting infection risk of airborne foot-and-mouth disease. Journal of the Royal Society Interface 6(34): 455-462

Gent, D.H.; Ocamb, C.M. 2009: Predicting infection risk of hop by Pseudoperonospora humuli. Phytopathology 99(10): 1190-1198

Jarvie, M.E.; Hand, D.W. 2009: Predicting influent estradiol and estrone concentrations for wastewater treatment facilities. Water Environment Research: a Research Publication of the Water Environment Federation 81(2): 131-139

Keijzers, G.B.; Vossen, C.Nathaniel.Kai-Lik.; Zhang, P.; Macbeth, D.; Derrington, P.; Gerrard, J.Gregory.; Doust, J. 2011: Predicting influenza A and 2009 H1N1 influenza in patients admitted to hospital with acute respiratory illness. Emergency Medicine Journal: Emj 28(6): 500-506

Yu, D.S.F.; Low, L.P.L.; Lee, I.F.K.; Lee, D.T.F.; Ng, W.M. 2014: Predicting influenza vaccination intent among at-risk chinese older adults in Hong Kong. Nursing Research 63(4): 270-277

Janssen, M.; Vereecken, K.M.; Geeraerd, A.H.; Logist, F.; De Visscher, Y.; Cappuyns, A.; Devlieghere, F.; Debevere, J.; Van Impe, J.F. 2003: Predicting inhibition and inactivation of Yersinia enterocolitica through lactic acid production by Lactobacillus sakei. Communications in Agricultural and Applied Biological Sciences 68(2): 449-457

Chekmarev, D.; Kholodovych, V.; Kortagere, S.; Welsh, W.J.; Ekins, S. 2009: Predicting inhibitors of acetylcholinesterase by regression and classification machine learning approaches with combinations of molecular descriptors. Pharmaceutical Research 26(9): 2216-2224

Roeg, D.; van de Goor, I.; Garretsen, H. 2015: Predicting initial client engagement with community mental health services by routinely measured data. Community Mental Health Journal 51(1): 71-78

Edmonds, E.W.; Capelo, R.M.; Stearns, P.; Bastrom, T.P.; Wallace, C.D.; Newton, P.O. 2009: Predicting initial treatment failure of fiberglass casts in pediatric distal radius fractures: utility of the second metacarpal-radius angle. Journal of Children's Orthopaedics 3(5): 375-381

Melzer, I.; Kurz, I.; Shahar, D.; Oddsson, L.I.E. 2009: Predicting injury from falls in older adults: comparison of voluntary step reaction times in injured and noninjured fallers--a prospective study. Journal of the American Geriatrics Society 57(4): 743-745

Neufeld, E.; Perlman, C.M.; Hirdes, J.P. 2012: Predicting inpatient aggression using the InterRAi risk of harm to others clinical assessment protocol: a tool for risk assessment and care planning. Journal of Behavioral Health Services and Research 39(4): 472-480

Bekelis, K.; Missios, S.; MacKenzie, T.A.; Desai, A.; Fischer, A.; Labropoulos, N.; Roberts, D.W. 2014: Predicting inpatient complications from cerebral aneurysm clipping: the Nationwide Inpatient Sample 2005-2009. Journal of neurosurgery 120(3): 591-598

Neuner, T.; Schmid, R.; Wolfersdorf, M.; Spiessl, H. 2008: Predicting inpatient suicides and suicide attempts by using clinical routine data?. General Hospital Psychiatry 30(4): 324-330

Bell, J.R.; Aralimarad, P.; Lim, K.-S.; Chapman, J.W. 2013: Predicting insect migration density and speed in the daytime convective boundary layer. Plos one 8(1): E54202

Kokras, N.; Kouzoupis, A.V.; Paparrigopoulos, T.; Ferentinos, P.; Karamanakos, P.; Kontoyannis, D.A.; Papadimitriou, G.N. 2011: Predicting insomnia in medical wards: the effect of anxiety, depression and admission diagnosis. General Hospital Psychiatry 33(1): 78-81

Afram, B.; Verbeek, H.; Bleijlevens, M.H.C.; Challis, D.; Leino-Kilpi, H.; Karlsson, S.; Soto, M.E.; Renom-Guiteras, A.; Saks, K.; Zabalegui, A.; Hamers, J.P.H.; Meyer, G.; Stephan, A.; Renom Guiteras, A.; Sauerland, D.; Wübker, A.; Bremer, P.; Hamers, P.; Afram, B.; Beerens, H.C.; Bleijlevens, M.H.C.; Verbeek, H.; Zwakhalen, S.M.G.; Ruwaard, D.; Ambergen, T.; Hallberg, I.Rahm.; Emilsson, U.Melin.; Karlsson, S.; Bokberg, C.; Lethin, C.; Challis, D.; Sutcliffe, C.; Jolley, D.; Tucker, S.; Bowns, I.; Roe, B.; Burns, A.; Leino-Kilpi, H.; Koskenniemi, J.; Suhonen, R. 2015: Predicting institutional long-term care admission in dementia: a mixed-methods study of informal caregivers' reports. Journal of Advanced Nursing 71(6): 1351-1362

Fitzgerald, S.; Gray, N.S.; Alexander, R.T.; Bagshaw, R.; Chesterman, P.; Huckle, P.; Jones, S.K.; Taylor, J.; Williams, T.; Snowden, R.J. 2013: Predicting institutional violence in offenders with intellectual disabilities: the predictive efficacy of the VRAG and the HCR-20. Journal of Applied Research in Intellectual Disabilities: Jarid 26(5): 384-393

Eum, R.S.; Seel, R.T.; Goldstein, R.; Brown, A.W.; Watanabe, T.K.; Zasler, N.D.; Roth, E.J.; Zafonte, R.D.; Glenn, M.B. 2015: Predicting institutionalization after traumatic brain injury inpatient rehabilitation. Journal of Neurotrauma 32(4): 280-286

Moreira, S.ér.R.; Ferreira, A.P.; Lima, R.M.; Arsa, G.; Campbell, C.S.G.; Simões, H.G.; Pitanga, F.J.G.; França, N.M. 2008: Predicting insulin resistance in children: anthropometric and metabolic indicators. Jornal de Pediatria 84(1): 47-52

Chiang, J.-K.; Lai, N.-S.; Chang, J.-K.; Koo, M. 2011: Predicting insulin resistance using the triglyceride-to-high-density lipoprotein cholesterol ratio in Taiwanese adults. Cardiovascular Diabetology 10: 93

Thornton, S.; Calam, R. 2011: Predicting intention to attend and actual attendance at a universal parent-training programme: a comparison of social cognition models. Clinical child psychology and psychiatry 16(3): 365-383

Astrøm, A.N.; Nasir, E.F. 2009: Predicting intention to treat HIV-infected patients among Tanzanian and Sudanese medical and dental students using the theory of planned behaviour--a cross sectional study. Bmc Health Services Research 9: 213

Abamecha, F.; Godesso, A.; Girma, E. 2013: Predicting intention to use voluntary HIV counseling and testing services among health professionals in Jimma, Ethiopia, using the theory of planned behavior. Journal of Multidisciplinary Healthcare 6: 399-407

Scholz, U.; Klaghofer, R.; Dux, R.; Roellin, M.; Boehler, A.; Muellhaupt, B.; Noll, G.; Wüthrich, R.P.; Goetzmann, L. 2012: Predicting intentions and adherence behavior in the context of organ transplantation: gender differences of provided social support. Journal of Psychosomatic Research 72(3): 214-219

Moan, I.S.øv.; Rise, J. 2011: Predicting intentions not to "drink and drive" using an extended version of the theory of planned behaviour. Accident; Analysis and Prevention 43(4): 1378-1384

Shapiro, M.A.; Porticella, N.; Jiang, L.C.; Gravani, R.B. 2011: Predicting intentions to adopt safe home food handling practices. Applying the theory of planned behavior. Appetite 56(1): 96-103

Bai, Y.; Wunderlich, S.M.; Fly, A.D. 2011: Predicting intentions to continue exclusive breastfeeding for 6 months: a comparison among racial/ethnic groups. Maternal and Child Health Journal 15(8): 1257-1264

Robinson, N.G.; Masser, B.M.; White, K.M.; Hyde, M.K.; Terry, D.J. 2008: Predicting intentions to donate blood among nondonors in Australia: an extended theory of planned behavior. Transfusion 48(12): 2559-2567

Pawlak, R.; Malinauskas, B.; Rivera, D. 2009: Predicting intentions to eat a healthful diet by college baseball players: applying the theory of planned behavior. Journal of nutrition education and behavior 41(5): 334-339

Smith-McLallen, A.; Fishbein, M. 2009: Predicting intentions to engage in cancer prevention and detection behaviors: examining differences between Black and White adults. Psychology Health and Medicine 14(2): 180-189

Groth, G.N. 2011: Predicting intentions to use research evidence for carpal tunnel syndrome treatment decisions among certified hand therapists. Journal of Occupational Rehabilitation 21(4): 559-572

Whitford, T.J.; Kubicki, M.; Ghorashi, S.; Schneiderman, J.S.; Hawley, K.J.; McCarley, R.W.; Shenton, M.E.; Spencer, K.M. 2011: Predicting inter-hemispheric transfer time from the diffusion properties of the corpus callosum in healthy individuals and schizophrenia patients: a combined ERP and DTi study. Neuroimage 54(3): 2318-2329

Pawelczyk, S.; Scott, K.A.; Hamer, R.; Blades, G.; Deane, C.M.; Wadhams, G.H. 2012: Predicting inter-species cross-talk in two-component signalling systems. Plos one 7(5): E37737

Kern, C.; González, A.J.; Liao, L.; Vijay-Shanker, K. 2013: Predicting interacting residues using long-distance information and novel decoding in hidden Markov models. IEEE Transactions on Nanobioscience 12(3): 158-164

Scarabelli, G.; Morra, G.; Colombo, G. 2010: Predicting interaction sites from the energetics of isolated proteins: a new approach to epitope mapping. Biophysical Journal 98(9): 1966-1975

Griffith, G.P.; Fulton, E.A.; Gorton, R.; Richardson, A.J. 2012: Predicting interactions among fishing, ocean warming, and ocean acidification in a marine system with whole-ecosystem models. Conservation Biology: the Journal of the Society for Conservation Biology 26(6): 1145-1152

Roomp, K.; Domingues, F.S. 2011: Predicting interactions between T cell receptors and MHC-peptide complexes. Molecular Immunology 48(4): 553-562

Hajingabo, L.J.; Daakour, S.; Martin, M.; Grausenburger, R.; Panzer-Grümayer, R.; Dequiedt, F.; Simonis, N.; Twizere, J.-C. 2014: Predicting interactome network perturbations in human cancer: application to gene fusions in acute lymphoblastic leukemia. Molecular Biology of the Cell 25(24): 3973-3985

Park, L.E.; Calogero, R.M.; Harwin, M.J.; DiRaddo, A.Marie. 2009: Predicting interest in cosmetic surgery: interactive effects of appearance-based rejection sensitivity and negative appearance comments. Body Image 6(3): 186-193

Jávo, I.á M.ár.á; Pettersen, G.; Rosenvinge, J.H.; Sørlie, T. 2012: Predicting interest in liposuction among women with eating problems: a population-based study. Body Image 9(1): 131-136

Shah, V.; Broseta, D. 2007: Predicting interfacial tension between water and nonpolar fluids from a Cahn-type theory. Langmuir 23(25): 12598-12605

Brasier, A.R.; Victor, S.; Ju, H.; Busse, W.W.; Curran-Everett, D.; Bleecker, E.; Castro, M.; Chung, K.F.; Gaston, B.; Israel, E.; Wenzel, S.E.; Erzurum, S.C.; Jarjour, N.N.; Calhoun, W.J. 2010: Predicting intermediate phenotypes in asthma using bronchoalveolar lavage-derived cytokines. Clinical and Translational Science 3(4): 147-157

Beckstrand, J.; Cirgin Ellett, M.L.; McDaniel, A. 2007: Predicting internal distance to the stomach for positioning nasogastric and orogastric feeding tubes in children. Journal of Advanced Nursing 59(3): 274-289

Mäntymaa, M.; Puura, K.; Luoma, I.; Latva, R.; Salmelin, R.K.; Tamminen, T. 2012: Predicting internalizing and externalizing problems at five years by child and parental factors in infancy and toddlerhood. Child Psychiatry and Human Development 43(2): 153-170

Muhtadie, L.; Zhou, Q.; Eisenberg, N.; Wang, Y. 2013: Predicting internalizing problems in Chinese children: the unique and interactive effects of parenting and child temperament. Development and Psychopathology 25(3): 653-667

Schabort, I.; Mercuri, M.; Grierson, L.E.M. 2014: Predicting international medical graduate success on college certification examinations: responding to the Thomson and Cohl judicial report on IMG selection. Canadian family physician Medecin de famille canadien 60(10): e478-e484

Paul, B. 2009: Predicting internet pornography use and arousal: the role of individual difference variables. Journal of Sex Research 46(4): 344-357

Carlert, S.; Pålsson, A.; Hanisch, G.; von Corswant, C.; Nilsson, C.; Lindfors, L.; Lennernäs, H.; Abrahamsson, B. 2010: Predicting intestinal precipitation--a case example for a basic BCS class Ii drug. Pharmaceutical Research 27(10): 2119-2130

Hobohm, C.; Fritzsch, D.; Budig, S.; Classen, J.; Hoffmann, K.-T.; Michalski, D. 2014: Predicting intracerebral hemorrhage by baseline magnetic resonance imaging in stroke patients undergoing systemic thrombolysis. Acta Neurologica Scandinavica 130(5): 338-345

Chi, J.H. 2012: Predicting intracerebral hemorrhage growth with a spot. Neurosurgery 71(2): N28-N29

Fabbri, A.; Servadei, F.; Marchesini, G.; Stein, S.C.; Vandelli, A. 2010: Predicting intracranial lesions by antiplatelet agents in subjects with mild head injury. Journal of Neurology Neurosurgery and Psychiatry 81(11): 1275-1279

Sivashanmugham, T.; Jahirdar, S.Mahamud.; Parthasarathy, S.; Muthurangan, G. 2013: Predicting intraoperative cardiovascular complication in patients with anterior mediastinal mass-role of central venous pressure monitoring. Indian Journal of Anaesthesia 57(4): 406-408

Azabou, E.; Manel, V.ér.; Abelin-Genevois, K.; Andre-Obadia, N.; Cunin, V.; Garin, C.; Kohler, R.; Berard, J.ér.ôm.; Ulkatan, S. 2014: Predicting intraoperative feasibility of combined TES-mMEP and cSSEP monitoring during scoliosis surgery based on preoperative neurophysiological assessment. Spine Journal: Official Journal of the North American Spine Society 14(7): 1214-1220

Jones, J.P.; Korzekwa, K.R. 2013: Predicting intrinsic clearance for drugs and drug candidates metabolized by aldehyde oxidase. Molecular Pharmaceutics 10(4): 1262-1268

Seabloom, E.W.; Borer, E.T.; Buckley, Y.; Cleland, E.E.; Davies, K.; Firn, J.; Harpole, W.S.; Hautier, Y.; Lind, E.; MacDougall, A.; Orrock, J.L.; Prober, S.M.; Adler, P.; Alberti, J.; Anderson, T.M.; Bakker, J.D.; Biederman, L.A.; Blumenthal, D.; Brown, C.S.; Brudvig, L.A.; Caldeira, M.; Chu, C.; Crawley, M.J.; Daleo, P.; Damschen, E.I.; D'Antonio, C.M.; DeCrappeo, N.M.; Dickman, C.R.; Du, G.; Fay, P.A.; Frater, P.; Gruner, D.S.; Hagenah, N.; Hector, A.; Helm, A.; Hillebrand, H.; Hofmockel, K.S.; Humphries, H.C.; Iribarne, O.; Jin, V.L.; Kay, A.; Kirkman, K.P.; Klein, J.A.; Knops, J.M.H.; La Pierre, K.J.; Ladwig, L.M.; Lambrinos, J.G.; Leakey, A.D.B.; Li, Q.; Li, W.; McCulley, R.; Melbourne, B.; Mitchell, C.E.; Moore, J.L.; Morgan, J. 2013: Predicting invasion in grassland ecosystems: is exotic dominance the real embarrassment of richness?. Global Change Biology 19(12): 3677-3687

Ayvaci, M.U.S.; Alagoz, O.; Chhatwal, J.; Munoz del Rio, A.; Sickles, E.A.; Nassif, H.; Kerlikowske, K.; Burnside, E.S. 2014: Predicting invasive breast cancer versus DCIS in different age groups. Bmc Cancer 14: 584

Paini, D.R.; Bianchi, F.J.J.A.; Northfield, T.D.; De Barro, P.J. 2011: Predicting invasive fungal pathogens using invasive pest assemblages: testing model predictions in a virtual world. Plos one 6(10): E25695

Celebiler Cavusoglu, A.; Kilic, Y.; Saydam, S.; Canda, T.; Başkan, Z.; Sevinc, A.I.; Sakizli, M. 2009: Predicting invasive phenotype with CDH1, CDH13, CD44, and TIMP3 gene expression in primary breast cancer. Cancer Science 100(12): 2341-2345

Paterson, R.A.; Dick, J.T.A.; Pritchard, D.W.; Ennis, M.; Hatcher, M.J.; Dunn, A.M. 2015: Predicting invasive species impacts: a community module functional response approach reveals context dependencies. Journal of Animal Ecology 84(2): 453-463

Loranger, J.; Meyer, S.T.; Shipley, B.; Kattge, J.; Loranger, H.; Roscher, C.; Weisser, W.W. 2012: Predicting invertebrate herbivory from plant traits: evidence from 51 grassland species in experimental monocultures. Ecology 93(12): 2674-2682

Loranger, J.; Meyer, S.T.; Shipley, B.; Kattge, J.; Loranger, H.; Roscher, C.; Wirth, C.; Weisser, W.W. 2013: Predicting invertebrate herbivory from plant traits: polycultures show strong nonadditive effects. Ecology 94(7): 1499-1509

Wood, J.L.; Alleyne, E.; Mozova, K.; James, M. 2014: Predicting involvement in prison gang activity: street gang membership, social and psychological factors. Law and human behavior 38(3): 203-211

Tan, Z.-J.; Chen, S.-J. 2010: Predicting ion binding properties for RNA tertiary structures. Biophysical Journal 99(5): 1565-1576

Lin, H.; Ding, H. 2011: Predicting ion channels and their types by the dipeptide mode of pseudo amino acid composition. Journal of Theoretical Biology 269(1): 64-69

Parsons, D.F. 2014: Predicting ion specific capacitances of supercapacitors due to quantum ionic interactions. Journal of Colloid and Interface Science 427: 67-72

He, Z.; Chen, S.-J. 2012: Predicting ion-nucleic acid interactions by energy landscape-guided sampling. Journal of Chemical Theory and Computation 8(6): 2095-2101

Olynyk, J.K.; Gan, E.; Tan, T. 2009: Predicting iron overload in hyperferritinemia. Clinical Gastroenterology and Hepatology: the Official Clinical Practice Journal of the American Gastroenterological Association 7(3): 359-362

Frisch, D.R.; Giedrimas, E.; Mohanavelu, S.; Shui, A.; Ho, K.K.L.; Gibson, C.M.; Josephson, M.E.; Zimetbaum, P.J. 2009: Predicting irreversible left ventricular dysfunction after acute myocardial infarction. American Journal of Cardiology 103(9): 1206-1209

Meurer, W.J.; Sánchez, B.N.; Smith, M.A.; Lisabeth, L.D.; Majersik, J.J.; Brown, D.L.; Uchino, K.; Bonikowski, F.P.; Mendizabal, J.E.; Zahuranec, D.B.; Morgenstern, L.B. 2009: Predicting ischaemic stroke subtype from presenting systolic blood pressure: the BASIC Project. Journal of Internal Medicine 265(3): 388-396

Goodman, P.G.; Mehta, A.R.; Castresana, M.R. 2009: Predicting ischemic brain injury after intraoperative cardiac arrest during cardiac surgery using the BIS monitor. Journal of Clinical Anesthesia 21(8): 609-612

Lian, B.Q.; Keaney, J.F. 2010: Predicting ischemic heart disease in women: the value of endothelial function. Journal of the American College of Cardiology 55(16): 1697-1699

Bellin, M.D.; Blondet, J.J.; Beilman, G.J.; Dunn, T.B.; Balamurugan, A.N.; Thomas, W.; Sutherland, D.E.R.; Moran, A. 2010: Predicting islet yield in pediatric patients undergoing pancreatectomy and autoislet transplantation for chronic pancreatitis. Pediatric Diabetes 11(4): 227-234

Wolken, G.G.; Fossen, B.J.; Noh, A.; Arriaga, E.A. 2013: Predicting isoelectric points of nonfunctional mitochondria from Monte Carlo simulations of surface compositions. Langmuir 29(8): 2700-2707

Wagner, H.; Boström, K.; Rinke, B. 2011: Predicting isometric force from muscular activation using a physiologically inspired model. Biomechanics and Modeling in Mechanobiology 10(6): 955-961

Whittaker, P.B.; Wang, X.; Regenauer-Lieb, K.; Chua, H.T. 2013: Predicting isosteric heats for gas adsorption. Physical Chemistry Chemical Physics: Pccp 15(2): 473-482

Chen, S.-Y.; Doong, S.-H. 2008: Predicting item exposure parameters in computerized adaptive testing. British Journal of Mathematical and Statistical Psychology 61(Part 1): 75-91

Wilford, J.; McMahon, A.D.; Peters, J.; Pickvance, S.; Jackson, A.; Blank, L.; Craig, D.; O'Rourke, A.; Macdonald, E.B. 2008: Predicting job loss in those off sick. Occupational Medicine 58(2): 99-106

Hardin, E.E.; Donaldson, J.R. 2014: Predicting job satisfaction: a new perspective on person-environment fit. Journal of Counseling Psychology 61(4): 634-640

Jung, H.; Lee, J.-E.; Kim, J. 2013: Predicting job stress: a specific case of a female golf caddy in South Korea. Work 45(2): 183-189

McQueen, F.M.; Dalbeth, N. 2009: Predicting joint damage in rheumatoid arthritis using MRi scanning. Arthritis Research and Therapy 11(5): 124

Cipriano, L.E.; Chesworth, B.M.; Anderson, C.K.; Zaric, G.S. 2007: Predicting joint replacement waiting times. Health Care Management Science 10(2): 195-215

Weber, M.; Thompson-Schill, S.L.; Osherson, D.; Haxby, J.; Parsons, L. 2009: Predicting judged similarity of natural categories from their neural representations. Neuropsychologia 47(3): 859-868

Mallett, C.A.; Stoddard Dare, P.; Seck, M.M. 2009: Predicting juvenile delinquency: the nexus of childhood maltreatment, depression and bipolar disorder. Criminal Behaviour and Mental Health: Cbmh 19(4): 235-246

Ang, R.P.; Goh, D.H. 2013: Predicting juvenile offending: a comparison of data mining methods. International Journal of Offender Therapy and Comparative Criminology 57(2): 191-207

Hattori, K.; Wakabayashi, H.; Tamaki, K. 2008: Predicting key example compounds in competitors' patent applications using structural information alone. Journal of Chemical Information and Modeling 48(1): 135-142

Zaza, G. 2012: Predicting kidney graft survival: molecular biology has the key. Giornale Italiano di Nefrologia: Organo Ufficiale Della Societa Italiana di Nefrologia 29(3): 266

Krikov, S.; Khan, A.; Baird, B.C.; Barenbaum, L.L.; Leviatov, A.; Koford, J.K.; Goldfarb-Rumyantzev, A.S. 2007: Predicting kidney transplant survival using tree-based modeling. Asaio Journal 53(5): 592-600

Talreja, H.; Akbari, A.; White, C.A.; Ramsay, T.O.; Hiremath, S.; Knoll, G. 2014: Predicting kidney transplantation outcomes using proteinuria ascertained from spot urine samples versus timed urine collections. American Journal of Kidney Diseases: the Official Journal of the National Kidney Foundation 64(6): 962-968

Bøgebo, R.; Horn, H.; Olsen, J.V.; Gammeltoft, S.; Jensen, L.J.; Hansen, J.L.; Christensen, G.L. 2014: Predicting kinase activity in angiotensin receptor phosphoproteomes based on sequence-motifs and interactions. Plos one 9(4): E94672

Sciabola, S.; Stanton, R.V.; Wittkopp, S.; Wildman, S.; Moshinsky, D.; Potluri, S.; Xi, H. 2008: Predicting kinase selectivity profiles using Free-Wilson QSAR analysis. Journal of Chemical Information and Modeling 48(9): 1851-1867

Lai, A.C.W.; Nguyen Ba, A.N.; Moses, A.M. 2012: Predicting kinase substrates using conservation of local motif density. Bioinformatics 28(7): 962-969

Hoorn, E.J.; Meima, M.E. 2012: Predicting kinase-substrate interactions in the era of proteomics: focus on "Identifying protein kinase target preferences using mass spectrometry". American Journal of Physiology. Cell Physiology 303(7): C711-C712

Al Otaiba, S.; Puranik, C.; Rouby, A.D.; Greulich, L.; Folsom, J.S.; Lee, J. 2010: Predicting kindergartners' end of year spelling ability from their reading, alphabetic, vocabulary, and phonological awareness skills, and prior literacy experiences. Learning Disability Quarterly: Journal of the Division for Children with Learning Disabilities 33(3): 171-183

Bai, H.; Yang, K.; Yu, D.; Zhang, C.; Chen, F.; Lai, L. 2011: Predicting kinetic constants of protein-protein interactions based on structural properties. Proteins 79(3): 720-734

Syaichurrozi, I.; Budiyono; Sumardiono, S. 2013: Predicting kinetic model of biogas production and biodegradability organic materials: biogas production from vinasse at variation of COD/N ratio. Bioresource Technology 149: 390-397

Markopoulos, G.; Grunenberg, J.ör. 2013: Predicting kinetically unstable C-C bonds from the ground-state properties of a molecule. Angewandte Chemie 52(40): 10648-10651

Jørgensen, D.R.; Dam, E.B.; Lillholm, M. 2013: Predicting knee cartilage loss using adaptive partitioning of cartilage thickness maps. Computers in Biology and Medicine 43(8): 1045-1052

Zhao, D.; Sakoda, H.; Sawyer, W.G.; Banks, S.A.; Fregly, B.J. 2008: Predicting knee replacement damage in a simulator machine using a computational model with a consistent wear factor. Journal of Biomechanical Engineering 130(1): 011004

Maderbacher, G.ün.; Schaumburger, J.; Baier, C.; Zeman, F.; Springorum, H.-R.; Dornia, C.; Grifka, J.; Keshmiri, A. 2014: Predicting knee rotation by the projection overlap of the proximal fibula and tibia in long-leg radiographs. Knee Surgery Sports Traumatology Arthroscopy: Official Journal of the Esska 22(12): 2982-2988

Schjølberg, S.; Eadie, P.; Zachrisson, H.D.; Oyen, A.-S.; Prior, M. 2011: Predicting language development at age 18 months: data from the Norwegian Mother and Child Cohort Study. Journal of Developmental and Behavioral Pediatrics: Jdbp 32(5): 375-383

Payabvash, S.; Kamalian, S.; Fung, S.; Wang, Y.; Passanese, J.; Kamalian, S.; Souza, L.C.S.; Kemmling, A.; Harris, G.J.; Halpern, E.F.; González, R.G.; Furie, K.L.; Lev, M.H. 2010: Predicting language improvement in acute stroke patients presenting with aphasia: a multivariate logistic model using location-weighted atlas-based analysis of admission CT perfusion scans. AJNR. American Journal of Neuroradiology 31(9): 1661-1668

Josse, G.; Kherif, F.; Flandin, G.; Seghier, M.L.; Price, C.J. 2009: Predicting language lateralization from gray matter. Journal of Neuroscience: the Official Journal of the Society for Neuroscience 29(43): 13516-13523

Reilly, S.; Wake, M.; Ukoumunne, O.C.; Bavin, E.; Prior, M.; Cini, E.; Conway, L.; Eadie, P.; Bretherton, L. 2010: Predicting language outcomes at 4 years of age: findings from Early Language in Victoria Study. Pediatrics 126(6): e1530-e1537

Brady, N.C.; Thiemann-Bourque, K.; Fleming, K.; Matthews, K. 2013: Predicting language outcomes for children learning augmentative and alternative communication: child and environmental factors. Journal of Speech Language and Hearing Research: Jslhr 56(5): 1595-1612

Ortendahl, M. 2007: Predicting lapse when stopping smoking among pregnant and non-pregnant women. Journal of Obstetrics and Gynaecology: the Journal of the Institute of Obstetrics and Gynaecology 27(2): 138-143

Kim, J.; Lin, L.-C.; Swisher, J.A.; Haranczyk, M.; Smit, B. 2012: Predicting large CO2 adsorption in aluminosilicate zeolites for postcombustion carbon dioxide capture. Journal of the American Chemical Society 134(46): 18940-18943

Grochola, G.; Snook, I.K.; Russo, S.P. 2014: Predicting large area surface reconstructions using molecular dynamics methods. Journal of Chemical Physics 140(5): 054701

Zhang, J.; Kim, S.; Grewal, J.; Albert, P.S. 2012: Predicting large fetuses at birth: do multiple ultrasound examinations and longitudinal statistical modelling improve prediction?. Paediatric and Perinatal Epidemiology 26(3): 199-207

Palmer, D.S.; Jensen, F. 2011: Predicting large-scale conformational changes in proteins using energy-weighted normal modes. Proteins 79(10): 2778-2793

Liao, Z.M.; Spaeth, M.L.; Manes, K.; Adams, J.J.; Carr, C.W. 2010: Predicting laser-induced bulk damage and conditioning for deuterated potassium dihydrogen phosphate crystals using an absorption distribution model. Optics Letters 35(15): 2538-2540

Wedlake, L.J.; Thomas, K.; Lalji, A.; Blake, P.; Khoo, V.S.; Tait, D.; Andreyev, H.J.N. 2010: Predicting late effects of pelvic radiotherapy: is there a better approach?. International Journal of Radiation Oncology Biology Physics 78(4): 1163-1170

Larose, E.; Rodés-Cabau, J.; Pibarot, P.; Rinfret, Séphane.; Proulx, G.; Nguyen, C.M.; Déry, J-Pierre.; Gleeton, O.; Roy, L.; Noël, B.; Barbeau, Gérald.; Rouleau, J.; Boudreault, J-Rock.; Amyot, M.; De Larochellière, R.; Bertrand, O.F. 2010: Predicting late myocardial recovery and outcomes in the early hours of ST-segment elevation myocardial infarction traditional measures compared with microvascular obstruction, salvaged myocardium, and necrosis characteristics by cardiovascular magnetic resonance. Journal of the American College of Cardiology 55(22): 2459-2469

Barbot, M.; Albiger, N.; Koutroumpi, S.; Ceccato, F.; Frigo, A.C.; Manara, R.; Fassina, A.; Gardiman, M.P.; Scanarini, M.; Mantero, F.; Scaroni, C. 2013: Predicting late recurrence in surgically treated patients with Cushing's disease. Clinical Endocrinology 79(3): 394-401

Pesco, D.; O'Neill, D.K. 2012: Predicting later language outcomes from the Language use Inventory. Journal of Speech Language and Hearing Research: Jslhr 55(2): 421-434

Boekelheide, K.; Blumberg, B.; Chapin, R.E.; Cote, I.; Graziano, J.H.; Janesick, A.; Lane, R.; Lillycrop, K.; Myatt, L.; States, J.C.; Thayer, K.A.; Waalkes, M.P.; Rogers, J.M. 2012: Predicting later-life outcomes of early-life exposures. Environmental Health Perspectives 120(10): 1353-1361

Lowmaster, S.E.; Morey, L.C. 2012: Predicting law enforcement officer job performance with the Personality Assessment Inventory. Journal of Personality Assessment 94(3): 254-261

Cheng, T.; Rivard, B.; Sánchez-Azofeifa, A.G.; Féret, J.-B.; Jacquemoud, S.; Ustin, S.L. 2012: Predicting leaf gravimetric water content from foliar reflectance across a range of plant species using continuous wavelet analysis. Journal of Plant Physiology 169(12): 1134-1142

Roelofsen, H.D.; van Bodegom, P.M.; Kooistra, L.; Witte, J.-P.M. 2014: Predicting leaf traits of herbaceous species from their spectral characteristics. Ecology and Evolution 4(6): 706-719

Tipple, B.J.; Berke, M.A.; Hambach, B.; Roden, J.S.; Ehleringer, J.R. 2015: Predicting leaf wax n-alkane 2H/1H ratios: controlled water source and humidity experiments with hydroponically grown trees confirm predictions of Craig-Gordon model. Plant Cell and Environment 38(6): 1035-1047

Wysokinski, W.E.; Ammash, N.; Sobande, F.; Kalsi, H.; Hodge, D.; McBane, R.D. 2010: Predicting left atrial thrombi in atrial fibrillation. American Heart Journal 159(4): 665-671

Tandon, H.; LaSala, A.F. 2007: Predicting left ventricular end-diastolic pressure by echocardiography. American Journal of Cardiology 99(12): 1776

Koshy, S.K.G.; George, L.K. 2015: Predicting left ventricular function recovery after reperfusion in ST elevation myocardial infarction: can we balance cost and accuracy?. Echocardiography 32(4): 613-614

Fujishiro, T.; Nishiyama, T.; Hayashi, S.; Hashimoto, S.; Kurosaka, M.; Kanno, T.; Masuda, T. 2012: Predicting leg-length change after total hip arthroplasty by measuring preoperative hip flexion under general anaesthesia. Journal of Orthopaedic Surgery 20(3): 327-330

Bult, M.K.; Verschuren, O.; Lindeman, E.; Jongmans, M.J.; Westers, P.; Claassen, A.; Ketelaar, M. 2013: Predicting leisure participation of school-aged children with cerebral palsy: longitudinal evidence of child, family and environmental factors. Child: Care Health and Development 39(3): 374-380

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Hollister, J.W.; Milstead, W.B.; Urrutia, M.A. 2011: Predicting maximum lake depth from surrounding topography. Plos one 6(9): E25764

Kempes, C.P.; West, G.B.; Crowell, K.; Girvan, M. 2011: Predicting maximum tree heights and other traits from allometric scaling and resource limitations. Plos one 6(6): E20551

Oliver, A.; Mendizabal, J.A.; Ripoll, G.; Albertí, P.; Purroy, A. 2010: Predicting meat yields and commercial meat cuts from carcasses of young bulls of Spanish breeds by the SEUROP method and an image analysis system. Meat Science 84(4): 628-633

Konstorum, A.; Sprowl, S.A.; Waterman, M.L.; Lander, A.D.; Lowengrub, J.S. 2013: Predicting mechanism of biphasic growth factor action on tumor growth using a multi-species model with feedback control. Journal of Coupled Systems and Multiscale Dynamics 1(4): 459-467

Lee, M.J.; Cizik, A.M.; Hamilton, D.; Chapman, J.R. 2014: Predicting medical complications after spine surgery: a validated model using a prospective surgical registry. Spine Journal: Official Journal of the North American Spine Society 14(2): 291-299

Chang, I-Chiu.; Hsu, H-Mei. 2012: Predicting medical staff intention to use an online reporting system with modified unified theory of acceptance and use of technology. Telemedicine Journal and E-Health: the Official Journal of the American Telemedicine Association 18(1): 67-73

Lambe, P.; Bristow, D. 2011: Predicting medical student performance from attributes at entry: a latent class analysis. Medical Education 45(3): 308-316

Jones, M.; Humphreys, J.; Prideaux, D. 2009: Predicting medical students' intentions to take up rural practice after graduation. Medical Education 43(10): 1001-1009

Gelb, S.R.; Shapiro, R.J.; Thornton, W.J.L. 2010: Predicting medication adherence and employment status following kidney transplant: the relative utility of traditional and everyday cognitive approaches. Neuropsychology 24(4): 514-526

Tennyson, D.H. 2009: Predicting medication costs and usage: expenditures in a juvenile detention facility. Journal of Correctional Health Care: the Official Journal of the National Commission on Correctional Health Care 15(2): 98

Pergolizzi, J.V.; Ben-Joseph, R.; Chang, C.-L.; Hess, G. 2015: Predicting medication persistence to buprenorphine transdermal system. Pain Practice: the Official Journal of World Institute of Pain 15(2): 140-149

Kim, M.M.; Howard, D.L.; Kaufman, J.S.; Holmes, D. 2008: Predicting medication use in an elderly hypertensive sample: revisiting the Established Populations for Epidemiologic Studies of the Elderly Study. Journal of the National Medical Association 100(12): 1386-1393

Zheng, P.; Griswold, M.D.; Hassold, T.J.; Hunt, P.A.; Small, C.L.; Ye, P. 2010: Predicting meiotic pathways in human fetal oogenesis. Biology of Reproduction 82(3): 543-551

Mar, V.; Wolfe, R.; Kelly, J.W. 2011: Predicting melanoma risk for the Australian population. Australasian Journal of Dermatology 52(2): 109-116

Ku, T.; Lu, P.; Chan, C.; Wang, T.; Lai, S.; Lyu, P.; Hsiao, N. 2009: Predicting melting temperature directly from protein sequences. Computational Biology and Chemistry 33(6): 445-450

Li, X.; Bo, H.; Zhang, X.C.; Hartsuck, J.A.; Tang, J. 2010: Predicting memapsin 2 (β-secretase) hydrolytic activity. Protein Science: a Publication of the Protein Society 19(11): 2175-2185

Hayat, M.; Khan, A. 2011: Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition. Journal of Theoretical Biology 271(1): 10-17

Rodrigues, M.A.; Foster, J.K.; Verdile, G.; Joesbury, K.; Prince, R.; Devine, A.; Mehta, P.; Beilby, J.; Martins, R.N. 2010: Predicting memory decline as a risk factor for Alzheimer's disease in older post-menopausal women: quod erat demonstrandum?. International Psychogeriatrics 22(2): 332-335

Wahlheim, C.N. 2011: Predicting memory performance under conditions of proactive interference: immediate and delayed judgments of learning. Memory and Cognition 39(5): 827-838

Langbaum, J.B.S.; Rebok, G.W.; Bandeen-Roche, K.; Carlson, M.C. 2009: Predicting memory training response patterns: results from ACTIVE. Journals of Gerontology. Series B Psychological Sciences and Social Sciences 64(1): 14-23

Li, M.-X.; Kwan, J.S.H.; Bao, S.-Y.; Yang, W.; Ho, S.-L.; Song, Y.-Q.; Sham, P.C. 2013: Predicting mendelian disease-causing non-synonymous single nucleotide variants in exome sequencing studies. Plos Genetics 9(1): E1003143

Ramezani Tehrani, F.; Dólleman, M.; van Disseldorp, J.; Broer, S.L.; Azizi, F.; Solaymani-Dodaran, M.; Fauser, B.C.; Laven, J.S.E.; Eijkemans, M.J.C.; Broekmans, F. 2014: Predicting menopausal age with anti-Müllerian hormone: a cross-validation study of two existing models. Climacteric: the Journal of the International Menopause Society 17(5): 583-590

Wilhelm, K.; Wedgwood, L.; Parker, G.; Geerligs, L.; Hadzi-Pavlovic, D. 2010: Predicting mental health and well-being in adulthood. Journal of Nervous and Mental Disease 198(2): 85-90

Lelorain, S.; Tessier, P.; Florin, A.ès.; Bonnaud-Antignac, A.él. 2011: Predicting mental quality of life in breast cancer survivors using comparison participants. Journal of Psychosocial Oncology 29(4): 430-449

Heinz, G.H.; Hoffman, D.J.; Klimstra, J.D.; Stebbins, K.R. 2010: Predicting mercury concentrations in mallard eggs from mercury in the diet or blood of adult females and from duckling down feathers. Environmental toxicology and chemistry 29(2): 389-392

Weller, H. 2009: Predicting mesh density for adaptive modelling of the global atmosphere. Philosophical Transactions. Series A Mathematical Physical and Engineering Sciences 367(1907): 4523-4542

Hall, K.D. 2010: Predicting metabolic adaptation, body weight change, and energy intake in humans. American Journal of Physiology. Endocrinology and Metabolism 298(3): E449-E466

Frey, M.A.; Templin, T.; Ellis, D.; Gutai, J.; Podolski, C.-L. 2007: Predicting metabolic control in the first 5 yr after diagnosis for youths with type 1 diabetes: the role of ethnicity and family structure. Pediatric Diabetes 8(4): 220-227

Tepper, N.; Shlomi, T. 2010: Predicting metabolic engineering knockout strategies for chemical production: accounting for competing pathways. Bioinformatics 26(4): 536-543

Van Berlo, R.J.P.; de Ridder, D.; Daran, J.-M.; Daran-Lapujade, P.A.S.; Teusink, B.; Reinders, M.J.T. 2011: Predicting metabolic fluxes using gene expression differences as constraints. Ieee/Acm Transactions on Computational Biology and Bioinformatics 8(1): 206-216

Faust, K.; van Helden, J. 2012: Predicting metabolic pathways by sub-network extraction. Methods in Molecular Biology 804: 107-130

Gao, Y.-F.; Chen, L.; Cai, Y.-D.; Feng, K.-Y.; Huang, T.; Jiang, Y. 2012: Predicting metabolic pathways of small molecules and enzymes based on interaction information of chemicals and proteins. Plos one 7(9): E45944

Weyand, P.G.; Smith, B.R.; Schultz, N.S.; Ludlow, L.W.; Puyau, M.R.; Butte, N.F. 2013: Predicting metabolic rate across walking speed: one fit for all body sizes?. Journal of Applied Physiology 115(9): 1332-1342

Liu, H.; Hsu, C.-H.; Lin, J.-D.; Hsieh, C.-H.; Lian, W.-C.; Wu, C.-Z.; Pei, D.; Chen, Y.-L. 2014: Predicting metabolic syndrome by using hematogram models in elderly women. Platelets 25(2): 97-101

Santoro, N.; Amato, A.; Grandone, A.; Brienza, C.; Savarese, P.; Tartaglione, N.; Marzuillo, P.; Perrone, L.; Miraglia Del Giudice, E. 2013: Predicting metabolic syndrome in obese children and adolescents: look, measure and ask. Obesity Facts 6(1): 48-56

Bielohuby, M.; Bodendorf, K.; Brandstetter, H.; Bidlingmaier, M.; Kienzle, E. 2010: Predicting metabolisable energy in commercial rat diets: physiological fuel values may be misleading. British Journal of Nutrition 103(10): 1525-1533

Zhu, D.; Zhong, Y.; Wu, H.; Ye, L.; Wang, J.; Li, Y.; Wei, Y.; Ren, L.; Xu, B.; Xu, J.; Qin, X. 2013: Predicting metachronous liver metastasis from colorectal cancer using serum proteomic fingerprinting. Journal of Surgical Research 184(2): 861-866

Passerini, A.; Lippi, M.; Frasconi, P. 2012: Predicting metal-binding sites from protein sequence. Ieee/Acm Transactions on Computational Biology and Bioinformatics 9(1): 203-213

Razavi, A.R.; Gill, H.; Ahlfeldt, H.; Shahsavar, N. 2007: Predicting metastasis in breast cancer: comparing a decision tree with domain experts. Journal of Medical Systems 31(4): 263-273

Ruf, C.G.; Linbecker, M.; Port, M.; Riecke, A.; Schmelz, H.U.; Wagner, W.; Meineke, V.; Abend, M. 2012: Predicting metastasized seminoma using gene expression. Bju International 110(2 Part 2): E14-E20

Hanazono, K.; Fukumoto, S.; Hirayama, K.; Takashima, K.; Yamane, Y.; Natsuhori, M.; Kadosawa, T.; Uchide, T. 2012: Predicting metastatic potential of gastrointestinal stromal tumors in dog by ultrasonography. Journal of Veterinary Medical Science 74(11): 1477-1482

Zhou, X.; Li, Z.; Dai, Z.; Zou, X. 2011: Predicting methylation status of human DNA sequences by pseudo-trinucleotide composition. Talanta 85(2): 1143-1147

Johnston, B.A.; Coghill, D.; Matthews, K.; Steele, J.D. 2015: Predicting methylphenidate response in attention deficit hyperactivity disorder: a preliminary study. Journal of Psychopharmacology 29(1): 24-30

Conklin, H.M.; Helton, S.; Ashford, J.; Mulhern, R.K.; Reddick, W.E.; Brown, R.; Bonner, M.; Jasper, B.W.; Wu, S.; Xiong, X.; Khan, R.B. 2010: Predicting methylphenidate response in long-term survivors of childhood cancer: a randomized, double-blind, placebo-controlled, crossover trial. Journal of Pediatric Psychology 35(2): 144-155

Lin, K.; Qian, Z.; Lu, L.; Lu, L.; Lai, L.; Gu, J.; Zeng, Z.; Li, H.; Cai, Y. 2010: Predicting miRNA's target from primary structure by the nearest neighbor algorithm. Molecular Diversity 14(4): 719-729

Song, X.; Cheng, L.; Zhou, T.; Guo, X.; Zhang, X.; Chen, Y.-p.P.; Han, P.; Sha, J. 2012: Predicting miRNA-mediated gene silencing mode based on miRNA-target duplex features. Computers in Biology and Medicine 42(1): 1-7

Albertini, M.C.; Olivieri, F.; Lazzarini, R.; Pilolli, F.; Galli, F.; Spada, G.; Accorsi, A.; Rippo, M.R.; Procopio, A.D. 2011: Predicting microRNA modulation in human prostate cancer using a simple String IDentifier (SID1.0). Journal of Biomedical Informatics 44(4): 615-620

Hsieh, C.-H.; Chang, D.T.-H.; Hsueh, C.-H.; Wu, C.-Y.; Oyang, Y.-J. 2010: Predicting microRNA precursors with a generalized Gaussian components based density estimation algorithm. Bmc Bioinformatics 11(Suppl 1): S52

Parker, B.J.; Wen, J. 2009: Predicting microRNA targets in time-series microarray experiments via functional data analysis. Bmc Bioinformatics 10(Suppl 1): S32

Van de Leemput, I.A.; Veraart, A.J.; Dakos, V.; de Klein, J.J.M.; Strous, M.; Scheffer, M. 2011: Predicting microbial nitrogen pathways from basic principles. Environmental Microbiology 13(6): 1477-1487

Hampson, D.; Crowther, J.; Bateman, I.; Kay, D.; Posen, P.; Stapleton, C.; Wyer, M.; Fezzi, C.; Jones, P.; Tzanopoulos, J. 2010: Predicting microbial pollution concentrations in UK rivers in response to land use change. Water Research 44(16): 4748-4759

Galyean, M.L.; Tedeschi, L.O. 2014: Predicting microbial protein synthesis in beef cattle: relationship to intakes of total digestible nutrients and crude protein. Journal of Animal Science 92(11): 5099-5111

Saylor, D.M.; Guyer, J.E.; Wheeler, D.; Warren, J.A. 2011: Predicting microstructure development during casting of drug-eluting coatings. Acta Biomaterialia 7(2): 604-613

Li, Y.C.; Shi, R.P.; Wang, C.P.; Liu, X.J.; Wang, Y. 2011: Predicting microstructures in polymer blends under two-step quench in two-dimensional space. Physical Review. e Statistical Nonlinear and Soft Matter Physics 83(4 Part 1): 041502

Neizel, M.; Futterer, S.; Steen, H.; Giannitsis, E.; Reinhardt, L.; Lossnitzer, D.; Lehrke, S.; Jaffe, A.S.; Katus, H.A. 2009: Predicting microvascular obstruction with cardiac troponin T after acute myocardial infarction: a correlative study with contrast-enhanced magnetic resonance imaging. Clinical Research in Cardiology: Official Journal of the German Cardiac Society 98(9): 555-562

Tokuda, Y.; Song, M.-H.; Oshima, H.; Usui, A.; Ueda, Y. 2008: Predicting midterm coronary artery bypass graft failure by intraoperative transit time flow measurement. Annals of Thoracic Surgery 86(2): 532-536

Kim, C.C.; Bogart, M.M.; Wee, S.A.; Burstein, R.; Arndt, K.A.; Dover, J.S. 2010: Predicting migraine responsiveness to botulinum toxin type a injections. Archives of Dermatology 146(2): 159-163

Wojcik, S.M. 2014: Predicting mild traumatic brain injury patients at risk of persistent symptoms in the Emergency Department. Brain Injury 28(4): 422-430

Tedeschi, L.O.; Fox, D.G. 2009: Predicting milk and forage intake of nursing calves. Journal of Animal Science 87(10): 3380-3391

Lo, R.Y.; Jagust, W.J.; Aisen, P.; Jack, C.R.; Toga, A.W.; Beckett, L.; Gamst, A.; Soares, H.; Green, R.C.; Montine, T.; Thomas, R.G.; Donohue, M.; Walter, S.; Dale, A.; Bernstein, M.; Felmlee, J.; Fox, N.; Thompson, P.; Schuff, N.; Alexander, G.; DeCarli, C.; Bandy, D.; Chen, K.; Morris, J.; Lee, V.M.-Y.; Korecka, M.; Crawford, K.; Neu, S.; Harvey, D.; Kornak, J.; Saykin, A.J.; Foroud, T.M.; Potkin, S.; Shen, L.; Buckholtz, N.; Kaye, J.; Dolen, S.; Quinn, J.; Schneider, L.; Pawluczyk, S.; Spann, B.M.; Brewer, J.; Vanderswag, H.; Heidebrink, J.L.; Lord, J.L.; Petersen, R.; Johnson, K.; Doody, R.S.; Villanueva-Meyer, J.; Chowdhury, M.; Stern, Y.; Honig, L.S.; Bell, K.L.; Morris, J.C.; Mintun, M.A.; Schneider, S.; Marson, D.; Griffith, R.; Cl 2012: Predicting missing biomarker data in a longitudinal study of Alzheimer disease. Neurology 78(18): 1376-1382

Crespo, I.; Krishna, A.; Le Béchec, A.; del Sol, A. 2013: Predicting missing expression values in gene regulatory networks using a discrete logic modeling optimization guided by network stable states. Nucleic Acids Research 41(1): E8

Fielding, S.; Fayers, P.; Ramsay, C. 2010: Predicting missing quality of life data that were later recovered: an empirical comparison of approaches. Clinical Trials 7(4): 333-342

García, S.; Pis, J.J.; Rubiera, F.; Pevida, C. 2013: Predicting mixed-gas adsorption equilibria on activated carbon for precombustion CO2 capture. Langmuir 29(20): 6042-6052

Ortiz, A.él.U.; Springuel-Huet, M.-A.; Coudert, F.ço.-X.; Fuchs, A.H.; Boutin, A. 2012: Predicting mixture coadsorption in soft porous crystals: experimental and theoretical Study of CO2/CH4 in MIL-53(Al). Langmuir 28(1): 494-498

Kalu, C.A.; Umeora, O.U.; Egwuatu, E.V.; Okwor, A. 2012: Predicting mode of delivery using mid-pregnancy ultrasonographic measurement of cervical length. Nigerian Journal of Clinical Practice 15(3): 338-343

Annett, R.D.; Bender, B.G.; Skipper, B.; Allen, C. 2010: Predicting moderate improvement and decline in pediatric asthma quality of life over 24 months. Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment Care and Rehabilitation 19(10): 1517-1527

Russom, C.L.; Bradbury, S.P.; Broderius, S.J.; Hammermeister, D.J.; Drummond, R.A.; Veith, G.D. 2013: Predicting modes of toxic action from chemical structure. Environmental Toxicology and Chemistry 32(7): 1441-1442

Ray, D.; Nepstad, D.; Brando, P. 2010: Predicting moisture dynamics of fine understory fuels in a moist tropical rainforest system: results of a pilot study undertaken to identify proxy variables useful for rating fire danger. New Phytologist 187(3): 720-732

Hoffmann, R.; Schleyer, P.v.R.é; Schaefer, H.F. 2008: Predicting molecules--more realism, please!. Angewandte Chemie 47(38): 7164-7167

McGrath, S.P.; Micó, C.; Zhao, F.J.; Stroud, J.L.; Zhang, H.; Fozard, S. 2010: Predicting molybdenum toxicity to higher plants: estimation of toxicity threshold values. Environmental Pollution 158(10): 3085-3094

McGrath, S.P.; Micó, C.; Curdy, R.; Zhao, F.J. 2010: Predicting molybdenum toxicity to higher plants: influence of soil properties. Environmental Pollution 158(10): 3095-3102

Buford, W.L.; Andersen, C.R. 2006: Predicting moment arms in diarthroidal joints - 3D computer simulation capability and muscle-tendon model validation. Conference Proceedings: . Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference 2006: 3407-3410

Hsu, K-Yu.; Chau, G-Yang.; Lui, W-Yiu.; Tsay, S-Haw.; King, K-Liang.; Wu, C-Wun. 2009: Predicting morbidity and mortality after hepatic resection in patients with hepatocellular carcinoma: the role of Model for End-Stage Liver Disease score. World Journal of Surgery 33(11): 2412-2419

Kouijzer, S.; Michels, J.J.; van den Berg, M.; Gevaerts, V.S.; Turbiez, M.; Wienk, M.M.; Janssen, R.é A.J. 2013: Predicting morphologies of solution processed polymer:fullerene blends. Journal of the American Chemical Society 135(32): 12057-12067

Murphy, B.; Rogerson, M.; Worcester, M.; Elliott, P.; Higgins, R.; Le Grande, M.; Turner, A.; Goble, A. 2013: Predicting mortality 12 years after an acute cardiac event: comparison between inhospital and 2-month assessment of depressive symptoms in women. Journal of Cardiopulmonary Rehabilitation and Prevention 33(3): 160-167

Iyer, K.S.; Iyer, P.U. 2015: Predicting mortality after congenital heart surgeries: evaluation of the Aristotle and Risk Adjustment in Congenital Heart Surgery-1 risk prediction scoring systems: a retrospective single center analysis of 1150 patients. Annals of Cardiac Anaesthesia 17(4): 271-272

Matchett, S.C.; Castaldo, J.; Wasser, T.E.; Baker, K.; Mathiesen, C.; Rodgers, J. 2006: Predicting mortality after intracerebral hemorrhage: comparison of scoring systems and influence of withdrawal of care. Journal of Stroke and Cerebrovascular Diseases: the Official Journal of National Stroke Association 15(4): 144-150

Barnes, S.; Gott, M.; Payne, S.; Parker, C.; Seamark, D.; Gariballa, S.; Small, N. 2008: Predicting mortality among a general practice-based sample of older people with heart failure. Chronic Illness 4(1): 5-12

Emukule, G.O.; McMorrow, M.; Ulloa, C.; Khagayi, S.; Njuguna, H.N.; Burton, D.; Montgomery, J.M.; Muthoka, P.; Katz, M.A.; Breiman, R.F.; Mott, J.A. 2014: Predicting mortality among hospitalized children with respiratory illness in Western Kenya, 2009-2012. PloS one 9(3): e92968

Abisheganaden, J.; Ding, Y.Yoong.; Chong, W-Fung.; Heng, B-Hoon.; Lim, T.Keang. 2012: Predicting mortality among older adults hospitalized for community-acquired pneumonia: an enhanced confusion, urea, respiratory rate and blood pressure score compared with pneumonia severity index. Respirology 17(6): 969-975

Kerbauy, F.áb.R.; Morelli, L.R.; de Andrade, C.áu.T.; Lisboa, L.F.; Cendoroglo Neto, M.; Hamerschlak, N. 2012: Predicting mortality and cost of hematopoietic stem-cell transplantation. Einstein 10(1): 82-85

Gale, C.P.; White, J.E.S.; Hunter, A.; Owen, J.; Allen, J.; Watson, J.; Holbrook, I.; Durham, N.P.; Pye, M.P. 2011: Predicting mortality and hospital admission in patients with COPD: significance of NT pro-BNP, clinical and echocardiographic assessment. Journal of Cardiovascular Medicine 12(9): 613-618

Pepler, P.T.; Uys, D.W.; Nel, D.G. 2012: Predicting mortality and length-of-stay for neonatal admissions to private hospital neonatal intensive care units: a Southern African retrospective study. African Health Sciences 12(2): 166-173

Schoe, A.; Schippers, E.F.; Ebmeyer, S.; Struck, J.; Klautz, R.J.M.; de Jonge, E.; van Dissel, J.T. 2014: Predicting mortality and morbidity after elective cardiac surgery using vasoactive and inflammatory biomarkers with and without the EuroSCORE model. Chest 146(5): 1310-1318

Conway, B.; Webster, A.; Ramsay, G.; Morgan, N.; Neary, J.; Whitworth, C.; Harty, J. 2009: Predicting mortality and uptake of renal replacement therapy in patients with stage 4 chronic kidney disease. Nephrology Dialysis Transplantation: Official Publication of the European Dialysis and Transplant Association - European Renal Association 24(6): 1930-1937

Wildman, M.J.; Sanderson, C.; Groves, J.; Reeves, B.C.; Ayres, J.; Harrison, D.; Young, D.; Rowan, K. 2009: Predicting mortality for patients with exacerbations of COPD and Asthma in the COPD and Asthma Outcome Study (CAOS). Qjm: Monthly Journal of the Association of Physicians 102(6): 389-399

Fei, M.W.; Kim, E.J.; Sant, C.A.; Jarlsberg, L.G.; Davis, J.L.; Swartzman, A.; Huang, L. 2009: Predicting mortality from HIV-associated Pneumocystis pneumonia at illness presentation: an observational cohort study. Thorax 64(12): 1070-1076

Taylor, S.L.; Lawless, M.; Curri, T.; Sen, S.; Greenhalgh, D.G.; Palmieri, T.L. 2014: Predicting mortality from burns: the need for age-group specific models. Burns: Journal of the International Society for Burn Injuries 40(6): 1106-1115

Rodríguez, M. 2013: Predicting mortality from head injury: experience of Sancti Spíritus Province, Cuba. Medicc Review 15(3): 30-33

Cross, H.D. 2014: Predicting mortality from noncardiac surgery. Annals of Surgery 259(1): E1

Nugent, J.; Edmonds, A.; Lusiama, J.; Thompson, D.; Behets, F. 2014: Predicting mortality in HIV-infected children initiating highly active antiretroviral therapy in a resource-deprived setting. Pediatric Infectious Disease Journal 33(11): 1148-1155

Cárdenas, Aés.; Ginès, P. 2008: Predicting mortality in cirrhosis--serum sodium helps. New England Journal of Medicine 359(10): 1060-1062

Mohan, A.; Bollineni, S. 2007: Predicting mortality in critically ill obstetric patients requiring intensive care unit admission in India. Indian Journal of Medical Sciences 61(4): 175-177

Timmermans, J.; Nicol, A.; Kairinos, N.; Teijink, J.; Prins, M.; Navsaria, P. 2010: Predicting mortality in damage control surgery for major abdominal trauma. South African Journal of Surgery. Suid-Afrikaanse Tydskrif Vir Chirurgie 48(1): 6-9

Riquelme, R.; Jiménez, P.; Videla, A.J.; Lopez, H.; Chalmers, J.; Singanayagam, A.; Riquelme, M.; Peyrani, P.; Wiemken, T.; Arbo, G.; Benchetrit, G.; Rioseco, M.L.; Ayesu, K.; Klotchko, A.; Marzoratti, L.; Raya, M.; Figueroa, S.; Saavedra, F.; Pryluka, D.; Inzunza, C.; Torres, A.; Alvare, P.; Fernandez, P.; Barros, M.; Gomez, Y.; Contreras, C.; Rello, J.; Bordon, J.; Feldman, C.; Arnold, F.; Nakamatsu, R.; Riquelme, J.; Blasi, F.; Aliberti, S.; Cosentini, R.; Lopardo, G.; Gnoni, M.; Welte, T.; Saad, M.; Guardiola, J.; Ramirez, J. 2011: Predicting mortality in hospitalized patients with 2009 H1N1 influenza pneumonia. International Journal of Tuberculosis and Lung Disease: the Official Journal of the International Union Against Tuberculosis and Lung Disease 15(4): 542-546

Wagner, M.; Ansell, D.; Kent, D.M.; Griffith, J.L.; Naimark, D.; Wanner, C.; Tangri, N. 2011: Predicting mortality in incident dialysis patients: an analysis of the United Kingdom Renal Registry. American Journal of Kidney Diseases: the Official Journal of the National Kidney Foundation 57(6): 894-902

Nakwan, N.; Nakwan, N.; Wannaro, J. 2011: Predicting mortality in infants with persistent pulmonary hypertension of the newborn with the Score for Neonatal Acute Physiology-Version Ii (SNAP-II) in Thai neonates. Journal of Perinatal Medicine 39(3): 311-315

Wang, Q.; Mou, S.; Xu, W.; Qi, C.; Ni, Z. 2013: Predicting mortality in microscopic polyangiitis with renal involvement: a survival analysis based on 64 patients. Renal Failure 35(1): 82-87

Garcia, L.; Messaris, E. 2013: Predicting mortality in patients admitted with sepsis using endotoxin activity: will it help us see through the crystal ball?*. Critical Care Medicine 41(6): 1575-1576

Budinger, G.R.S.; Walley, K.R. 2011: Predicting mortality in patients with acute lung injury. American Journal of Respiratory and Critical Care Medicine 184(4): 394-395

Chiu, P.W.Y.; Ng, E.K.W.; Cheung, F.K.Y.; Chan, F.K.L.; Leung, W.K.; Wu, J.C.Y.; Wong, V.W.S.; Yung, M.Y.; Tsoi, K.; Lau, J.Y.W.; Sung, J.J.Y.; Chung, S.S.C. 2009: Predicting mortality in patients with bleeding peptic ulcers after therapeutic endoscopy. Clinical Gastroenterology and Hepatology: the Official Clinical Practice Journal of the American Gastroenterological Association 7(3): 311-316; Quiz 253

Ronan, D.; Nathwani, D.; Davey, P.; Barlow, G. 2010: Predicting mortality in patients with community-acquired pneumonia and low CURB-65 scores. European Journal of Clinical Microbiology and Infectious Diseases: Official Publication of the European Society of Clinical Microbiology 29(9): 1117-1124

Van Diepen, M.; Schroijen, M.A.; Dekkers, O.M.; Rotmans, J.I.; Krediet, R.T.; Boeschoten, E.W.; Dekker, F.W. 2014: Predicting mortality in patients with diabetes starting dialysis. Plos one 9(3): E89744

Marmo, R.; Koch, M.; Cipolletta, L.; Bianco, M.A.; Grossi, E.; Rotondano, G.; Pera, A.; Rocca, R.; Dezi, A.; Fasoli, R.; Brunati, S.; Lorenzini, I.; Germani, U.; Giorgio, P.; Imperiali, G.; Minoli, G.; Barberani, F.; Boschetto, S.; Martorano, M.; Gatto, G.; Amuso, M.; Pastorelli, A.; Torre, E.S.; Triossi, O.; Buzzi, A.; Cestari, R.; Della Casa, D.; Proietti, M.; Tanzilli, A.; Aragona, G.; Giangregorio, F.; Allegretta, L.; Tronci, S.; Michetti, P.; Romagnoli, P.; Nucci, A.; Rogai, F.; Piubello, W.; Tebaldi, M.; Bonfante, F.; Casadei, A.; Cortini, C.; Chiozzini, G.; Girardi, L.; Leoci, C.; Bagnalasta, G.; Segato, S.; Chianese, G.; Salvagnini, M.; Pandolfo, N.; Dezi, A.; Casetti, T.; Lorenzini, I.; Germani, U.; Imperiali, G.; Barberani, I.S.F. 2014: Predicting mortality in patients with in-hospital nonvariceal upper Gi bleeding: a prospective, multicenter database study. Gastrointestinal Endoscopy 79(5): 741-749.E1

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Stormark, K.M.; Heiervang, E.; Heimann, M.; Lundervold, A.; Gillberg, C. 2008: Predicting nonresponse bias from teacher ratings of mental health problems in primary school children. Journal of Abnormal Child Psychology 36(3): 411-419

Halliday-Boykins, C.A.; Schaeffer, C.M.; Henggeler, S.W.; Chapman, J.E.; Cunningham, P.B.; Randall, J.; Shapiro, S.B. 2010: Predicting nonresponse to juvenile drug court interventions. Journal of Substance Abuse Treatment 39(4): 318-328

Cody, H.S.; Van Zee, K.J. 2008: Predicting nonsentinel node metastases in sentinel node-positive breast cancer: what have we learned, can we do better, and do we need to?. Annals of Surgical Oncology 15(11): 2998-3002

Petukh, M.; Zhenirovskyy, M.; Li, C.; Li, L.; Wang, L.; Alexov, E. 2012: Predicting nonspecific ion binding using DelPhi. Biophysical Journal 102(12): 2885-2893

Ganapathi, A.; McCarron, J.A.; Chen, X.; Iannotti, J.P. 2011: Predicting normal glenoid version from the pathologic scapula: a comparison of 4 methods in 2- and 3-dimensional models. Journal of Shoulder and Elbow Surgery 20(2): 234-244

Dudai, Y. 2009: Predicting not to predict too much: how the cellular machinery of memory anticipates the uncertain future. Philosophical Transactions of the Royal Society of London. Series B Biological Sciences 364(1521): 1255-1262

Meyer, I.M. 2008: Predicting novel RNA-RNA interactions. Current Opinion in Structural Biology 18(3): 387-393

Vanni, S.; Neri, M.; Tavernelli, I.; Rothlisberger, U. 2011: Predicting novel binding modes of agonists to β adrenergic receptors using all-atom molecular dynamics simulations. Plos Computational Biology 7(1): E1001053

Helmy, M.; Gohda, J.; Inoue, J.-I.; Tomita, M.; Tsuchiya, M.; Selvarajoo, K. 2009: Predicting novel features of toll-like receptor 3 signaling in macrophages. Plos one 4(3): E4661

Done, B.; Khatri, P.; Done, A.; Drăghici, S. 2010: Predicting novel human gene ontology annotations using semantic analysis. Ieee/Acm Transactions on Computational Biology and Bioinformatics 7(1): 91-99

Schuster, S.; de Figueiredo, L.ís.F.; Kaleta, C. 2010: Predicting novel pathways in genome-scale metabolic networks. Biochemical Society Transactions 38(5): 1202-1205

Dror, I.; Shazman, S.; Mukherjee, S.; Zhang, Y.; Glaser, F.; Mandel-Gutfreund, Y. 2012: Predicting nucleic acid binding interfaces from structural models of proteins. Proteins 80(2): 482-489

Wu, J.; Zhang, Y.; Mu, Z. 2014: Predicting nucleosome positioning based on geometrically transformed Tsallis entropy. Plos one 9(11): E109395

Scipioni, A.; De Santis, P. 2011: Predicting nucleosome positioning in genomes: physical and bioinformatic approaches. Biophysical Chemistry 155(2-3): 53-64

Xi, L.; Fondufe-Mittendorf, Y.; Xia, L.; Flatow, J.; Widom, J.; Wang, J.-P. 2010: Predicting nucleosome positioning using a duration Hidden Markov Model. Bmc Bioinformatics 11: 346

Zhang, Z.; Zhang, Y.; Gutman, I. 2012: Predicting nucleosome positions in yeast: using the absolute frequency. Journal of Biomolecular Structure and Dynamics 29(5): 1081-1088

Teif, V.B.; Rippe, K. 2009: Predicting nucleosome positions on the DNA: combining intrinsic sequence preferences and remodeler activities. Nucleic Acids Research 37(17): 5641-5655

Lai, H.-W.; Huang, R.-H.; Wu, Y.-T.; Chen, C.-J.; Chen, S.-T.; Lin, Y.-J.; Chen, D.-R.; Lee, C.-W.; Wu, H.-K.; Lin, H.-Y.; Kuo, S.-J. 2018: Clinicopathologic factors related to surgical margin involvement, reoperation, and residual cancer in primary operable breast cancer - An analysis of 2050 patients. European Journal of Surgical Oncology: the Journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology 44(11): 1725-1735

Hansen, N.; Sverke, M.; Näswall, K. 2009: Predicting nurse burnout from demands and resources in three acute care hospitals under different forms of ownership: a cross-sectional questionnaire survey. International Journal of Nursing Studies 46(1): 95-106

Norten, A. 2012: Predicting nurses' acceptance of radiofrequency identification technology. Computers Informatics Nursing: Cin 30(10): 531-537; Quiz: 538-539

James, M.L.; Wiley, E.; Fries, B.E. 2007: Predicting nursing facility transition candidates using AID: a case study. Gerontologist 47(5): 625-632

Gaugler, J.E.; Duval, S.; Anderson, K.A.; Kane, R.L. 2007: Predicting nursing home admission in the U.S: a meta-analysis. Bmc Geriatrics 7: 13

Squires, A.; Beltrán-Sánchez, H. 2009: Predicting nursing human resources: an exploratory study. Policy Politics and Nursing Practice 10(2): 101-109

Wagner, C.M. 2010: Predicting nursing turnover with catastrophe theory. Journal of Advanced Nursing 66(9): 2071-2084

Herwig, A.; Schneider, W.X. 2014: Predicting object features across saccades: evidence from object recognition and visual search. Journal of Experimental Psychology. General 143(5): 1903-1922

Lee, T.; Hammad, M.; Chan, T.C.Y.; Craig, T.; Sharpe, M.B. 2013: Predicting objective function weights from patient anatomy in prostate IMRT treatment planning. Medical Physics 40(12): 121706

Sebire, S.J.; Standage, M.; Vansteenkiste, M. 2011: Predicting objectively assessed physical activity from the content and regulation of exercise goals: evidence for a mediational model. Journal of Sport and Exercise Psychology 33(2): 175-197

Schrock, J.W.; Laskey, S.; Cydulka, R.K. 2008: Predicting observation unit treatment failures in patients with skin and soft tissue infections. International Journal of Emergency Medicine 1(2): 85-90

Hoexter, M.Q.; Miguel, E.C.; Diniz, J.B.; Shavitt, R.G.; Busatto, G.F.; Sato, J.ão.R. 2013: Predicting obsessive-compulsive disorder severity combining neuroimaging and machine learning methods. Journal of Affective Disorders 150(3): 1213-1216

Sharkey, K.M.; Machan, J.T.; Tosi, C.; Roye, G.D.; Harrington, D.; Millman, R.P. 2010: Predicting obstructive sleep apnea among women candidates for bariatric surgery. Journal of Women's Health 19(10): 1833-1841

Alcock, C.; Layer, G.T. 2010: Predicting occult malignancy in nipple discharge. ANZ Journal of Surgery 80(9): 646-649

Tsivian, M.; Moreira, D.M.; Caso, J.R.; Mouraviev, V.; Madden, J.F.; Bratslavsky, G.; Robertson, C.N.; Albala, D.M.; Polascik, T.J. 2010: Predicting occult multifocality of renal cell carcinoma. European Urology 58(1): 118-126

Suarthana, E.; Meijer, E.; Grobbee, D.E.; Heederik, D. 2009: Predicting occupational diseases. Occupational and Environmental Medicine 66(11): 713-714

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Zhang, Y. 2008: Predicting occurrences of acute hypoglycemia during insulin therapy in the intensive care unit. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 2008: 3297-3300

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Park, C.Y.; Oh, J.-H.; Chuck, R.S. 2013: Predicting ocular residual astigmatism using corneal and refractive parameters: a myopic eye study. Current Eye Research 38(8): 851-861

Snitz, K.; Yablonka, A.; Weiss, T.; Frumin, I.; Khan, R.M.; Sobel, N. 2013: Predicting odor perceptual similarity from odor structure. Plos Computational Biology 9(9): E1003184

Haddad, R.; Medhanie, A.; Roth, Y.; Harel, D.; Sobel, N. 2010: Predicting odor pleasantness with an electronic nose. Plos Computational Biology 6(4): E1000740

Bachtiar, L.R.; Unsworth, C.P.; Newcomb, R.D.; Crampin, E.J. 2011: Predicting odorant chemical class from odorant descriptor values with an assembly of multi-layer perceptrons. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 2011: 2756-2759

Szabó, G.T.ás.; Herényi, G.; Szabó, G. 2007: Predicting of growth tendency with cephalometry. Cephalometric research comparing orthodontic treatment cases with four premolar extraction. Fogorvosi Szemle 100(1): 11-15

Mohebi, S.; Azadbakht, L.; Feizi, A.; Sharifirad, G.; Hozori, M. 2014: Predicting of perceived self efficacy in the amount of macronutrients intake in women with metabolic syndrome - 2012. Journal of Education and Health Promotion 3: 21

Kazemi, E.; Hosseini, S.M.; Bahrampour, A.; Faghihimani, E.; Amini, M. 2014: Predicting of trend of hemoglobin a1c in type 2 diabetes: a longitudinal linear mixed model. International Journal of Preventive Medicine 5(10): 1274-1280

Scialfa, C.; Ference, J.; Boone, J.; Tay, R.; Hudson, C. 2010: Predicting older adults' driving difficulties using the Roadwise Review. Journals of Gerontology. Series B Psychological Sciences and Social Sciences 65(4): 434-437

Classen, S.; Wang, Y.; Crizzle, A.M.; Winter, S.M.; Lanford, D.N. 2013: Predicting older driver on-road performance by means of the useful field of view and trail making test part B. American Journal of Occupational Therapy: Official Publication of the American Occupational Therapy Association 67(5): 574-582

Schmuker, M.; de Bruyne, M.; Hähnel, M.; Schneider, G. 2007: Predicting olfactory receptor neuron responses from odorant structure. Chemistry Central Journal 1: 11

Brockamp, T.; Nienaber, U.; Mutschler, M.; Wafaisade, A.; Peiniger, S.; Lefering, R.; Bouillon, B.; Maegele, M. 2012: Predicting on-going hemorrhage and transfusion requirement after severe trauma: a validation of six scoring systems and algorithms on the TraumaRegister DGU. Critical Care 16(4): R129

Hoggarth, P.A.; Innes, C.R.H.; Dalrymple-Alford, J.C.; Jones, R.D. 2013: Predicting on-road assessment pass and fail outcomes in older drivers with cognitive impairment using a battery of computerized sensory-motor and cognitive tests. Journal of the American Geriatrics Society 61(12): 2192-2198

Marek, R.J.; Ben-Porath, Y.S.; Merrell, J.; Ashton, K.; Heinberg, L.J. 2014: Predicting one and three month postoperative Somatic Concerns, Psychological Distress, and Maladaptive Eating Behaviors in bariatric surgery candidates with the Minnesota Multiphasic Personality Inventory-2 Restructured Form (MMPI-2-RF). Obesity Surgery 24(4): 631-639

Schwingel, P.A.; Porto, Y.C.; Dias, M.C.M.; Moreira, M.ôn.M.; Zoppi, C.áu.C. 2009: Predicting one repetition maximum equations accuracy in paralympic rowers with motor disabilities. Journal of Strength and Conditioning Research 23(3): 1045-1050

Parker, G. 2010: Predicting onset of bipolar disorder from subsyndromal symptoms: a signal question?. British Journal of Psychiatry: the Journal of Mental Science 196(2): 87-88

Creemers, H.E.; van Lier, P.A.C.; Vollebergh, W.A.M.; Ormel, J.; Verhulst, F.C.; Huizink, A.C. 2009: Predicting onset of cannabis use in early adolescence: the interrelation between high-intensity pleasure and disruptive behavior. the TRAILS Study. Journal of Studies on Alcohol and Drugs 70(6): 850-858

Takao, D.; Ibara, S.; Tokuhisa, T.; Ishihara, C.; Maede, Y.; Matsui, T.; Tokumasu, H.; Sato, K.; Hirakawa, E.; Kabayama, C.; Yamamoto, M. 2014: Predicting onset of chronic lung disease using cord blood cytokines. Pediatrics International: Official Journal of the Japan Pediatric Society 56(4): 566-570

Yamaguchi, M.; Fukami, T.; Asakura, H.; Takeshita, T. 2015: Predicting onset of labor from echogenicity of the cervical gland area on vaginal ultrasonography at term. Journal of Perinatal Medicine 43(5): 577-584

Waki, K.; Sugawara, Y.; Mizuta, K.; Taniguchi, M.; Ozawa, M.; Hirata, M.; Nozawa, M.; Kaneko, J.; Takahashi, K.; Kadowaki, T.; Terasaki, P.I.; Kokudo, N. 2013: Predicting operational tolerance in pediatric living-donor liver transplantation by absence of HLA antibodies. Transplantation 95(1): 177-183

Sim, A.Y.; Bowman, M.; Hopman, W.; Engen, D.; Silva, M.; James, P. 2014: Predicting operative bleeding in elective pediatric surgeries using the Pediatric Bleeding Questionnaire (PBQ). Journal of Pediatric Hematology/Oncology 36(4): E246-E247

Ialenti, M.N.; Lonner, B.S.; Verma, K.; Dean, L.; Valdevit, A.; Errico, T. 2013: Predicting operative blood loss during spinal fusion for adolescent idiopathic scoliosis. Journal of Pediatric Orthopedics 33(4): 372-376

Fedoruk, L.M.; Tribble, C.G.; Kern, J.A.; Peeler, B.B.; Kron, I.L. 2007: Predicting operative mortality after surgery for ischemic cardiomyopathy. Annals of Thoracic Surgery 83(6): 2029-2035; Discussion 2035

Turk, D.C.; Swanson, K.S.; Gatchel, R.J. 2008: Predicting opioid misuse by chronic pain patients: a systematic review and literature synthesis. Clinical Journal of Pain 24(6): 497-508

El Solh, A.; Akinnusi, M.; Patel, A.; Bhat, A.; TenBrock, R. 2009: Predicting optimal CPAP by neural network reduces titration failure: a randomized study. Sleep and Breathing 13(4): 325-330

Akahoshi, T.; Akashiba, T.; Kawahara, S.; Uematsu, A.; Nagaoka, K.; Kiyofuji, K.; Okamoto, N.; Hattori, T.; Takahashi, N.; Hashimoto, S. 2009: Predicting optimal continuous positive airway pressure in Japanese patients with obstructive sleep apnoea syndrome. Respirology 14(2): 245-250

Kanu, A.B.; Gribb, M.M.; Hill, H.H. 2008: Predicting optimal resolving power for ambient pressure ion mobility spectrometry. Analytical Chemistry 80(17): 6610-6619

Sinha, V.K.; De Buck, S.S.; Fenu, L.A.; Smit, J.W.; Nijsen, M.; Gilissen, R.A.H.J.; Van Peer, A.; Lavrijsen, K.; Mackie, C.E. 2008: Predicting oral clearance in humans: how close can we get with allometry?. Clinical Pharmacokinetics 47(1): 35-45

Dempsey, A.R.; Johnson, S.S.; Westhoff, C.L. 2011: Predicting oral contraceptive continuation using the transtheoretical model of health behavior change. Perspectives on Sexual and Reproductive Health 43(1): 23-29

Rayan, A.; Marcus, D.; Goldblum, A. 2010: Predicting oral druglikeness by iterative stochastic elimination. Journal of Chemical Information and Modeling 50(3): 437-445

Aguilar-Díaz, J.E.; García-Montoya, E.; Suñe-Negre, J.é M.ía.; Pérez-Lozano, P.; Miñarro, M.; Ticó, J.é R.ón. 2012: Predicting orally disintegrating tablets formulations of ibuprophen tablets: an application of the new SeDeM-ODT expert system. European Journal of Pharmaceutics and Biopharmaceutics: Official Journal of Arbeitsgemeinschaft für Pharmazeutische Verfahrenstechnik E.V 80(3): 638-648

Németh, L.ác.J.; Hegedüs, Z.óf.; Martinek, T.ás.A. 2014: Predicting order and disorder for β-peptide foldamers in water. Journal of Chemical Information and Modeling 54(10): 2776-2783

Tekpinar, M.; Zheng, W. 2010: Predicting order of conformational changes during protein conformational transitions using an interpolated elastic network model. Proteins 78(11): 2469-2481

De Campos-Lobato, L.F.; Campos-Lobato, L.F.; Wells, B.; Wick, E.; Pronty, K.; Kiran, R.; Remzi, F.; Vogel, J.D. 2009: Predicting organ space surgical site infection with a nomogram. Journal of Gastrointestinal Surgery: Official Journal of the Society for Surgery of the Alimentary Tract 13(11): 1986-1992

Warren, J.J.; Mayer, J.M. 2010: Predicting organic hydrogen atom transfer rate constants using the Marcus cross relation. Proceedings of the National Academy of Sciences of the United States of America 107(12): 5282-5287

Huey, R.B.; Kearney, M.R.; Krockenberger, A.; Holtum, J.A.M.; Jess, M.; Williams, S.E. 2012: Predicting organismal vulnerability to climate warming: roles of behaviour, physiology and adaptation. Philosophical Transactions of the Royal Society of London. Series B Biological Sciences 367(1596): 1665-1679

Dávila, M.C.; Finkelstein, M.A. 2010: Predicting organizational citizenship behavior from the functional analysis and role identity perspectives: further evidence in Spanish employees. Spanish Journal of Psychology 13(1): 277-283

Yock, A.D.; Rao, A.; Dong, L.; Beadle, B.M.; Garden, A.S.; Kudchadker, R.J.; Court, L.E. 2014: Predicting oropharyngeal tumor volume throughout the course of radiation therapy from pretreatment computed tomography data using general linear models. Medical Physics 41(5): 051705

Giuggioli, L.; Potts, J.R.; Harris, S. 2012: Predicting oscillatory dynamics in the movement of territorial animals. Journal of the Royal Society Interface 9(72): 1529-1543

Timmer, M.H.M.; Samson, M.M.; Monninkhof, E.M.; De Ree, B.; Verhaar, H.J.J. 2009: Predicting osteoporosis in patients with a low-energy fracture. Archives of Gerontology and Geriatrics 49(1): E32-E35

Gammage, K.L.; Klentrou, P. 2011: Predicting osteoporosis prevention behaviors: health beliefs and knowledge. American Journal of Health Behavior 35(3): 371-382

Varda, B.K.; Schmid, M.; Trinh, Q.-D. 2014: Predicting other-cause mortality: the minimalistic approach. European Urology 66(6): 1010-1011

Sparenberg, P.; Springer, A.; Prinz, W. 2012: Predicting others' actions: evidence for a constant time delay in action simulation. Psychological Research 76(1): 41-49

Natale, E.; Senna, I.; Bolognini, N.; Quadrelli, E.; Addabbo, M.; Macchi Cassia, V.; Turati, C. 2014: Predicting others' intention involves motor resonance: EMG evidence from 6- and 9-month-old infants. Developmental Cognitive Neuroscience 7: 23-29

Wilfond, B. 2012: Predicting our future: lessons from Winnie-the-Pooh. Hastings Center Report 42(4): 3

Alvarez, C.A.; Clark, C.A.; Zhang, S.; Halm, E.A.; Shannon, J.J.; Girod, C.E.; Cooper, L.; Amarasingham, R. 2013: Predicting out of intensive care unit cardiopulmonary arrest or death using electronic medical record data. Bmc Medical Informatics and Decision Making 13: 28

Sheppard, J.P.; Holder, R.; Nichols, L.; Bray, E.; Hobbs, F.D.Richard.; Mant, J.; Little, P.; Williams, B.; Greenfield, S.; McManus, R.J. 2014: Predicting out-of-office blood pressure level using repeated measurements in the clinic: an observational cohort study. Journal of Hypertension 32(11): 2171-8; Discussion 2178

Albino, F.P.; Koltz, P.F.; Girotto, J.A. 2010: Predicting out-of-pocket costs in the surgical management of orofacial clefts. Plastic and Reconstructive Surgery 126(4): 188e-189e

Buckeridge, D.L.; Okhmatovskaia, A.; Tu, S.; O'Connor, M.; Nyulas, C.; Musen, M.A. 2008: Predicting outbreak detection in public health surveillance: quantitative analysis to enable evidence-based method selection. AMIA .. Annual Symposium Proceedings. AMIA Symposium 2008: 76-80

De Vadder, K.; Van De Bruaene, A.; Gewillig, M.; Meyns, B.; Troost, E.; Budts, W. 2014: Predicting outcome after Fontan palliation: a single-centre experience, using simple clinical variables. Acta Cardiologica 69(1): 7-14

He, Y.; Li, T.; Li, T.; Schonewille, W.J.; Greving, J.P.; Kappelle, L.J.; Algra, A. 2012: Predicting outcome after acute basilar artery occlusion based on admission characteristics. Neurology 79(13): 1410; author reply 1410

Appelboom, G.; Hwang, B.Y.; Bruce, S.S.; Piazza, M.A.; Kellner, C.P.; Meyers, P.M.; Connolly, E.S. 2012: Predicting outcome after arteriovenous malformation-associated intracerebral hemorrhage with the original ICH score. World Neurosurgery 78(6): 646-650

Maia, B.; Roque, R.; Amaral-Silva, A.; Lourenço, S.ón.; Bento, L.ís.; Alcântara, J.ão. 2013: Predicting outcome after cardiopulmonary arrest in therapeutic hypothermia patients: clinical, electrophysiological and imaging prognosticators. Acta Medica Portuguesa 26(2): 93-97

Forsyth, R.; Kirkham, F. 2012: Predicting outcome after childhood brain injury. Cmaj: Canadian Medical Association Journal 184(11): 1257-1264

Zheng, K.; Tan, J.-X.; Li, F.; Li, H.-Y.; Zeng, X.-H.; Ma, B.-L.; Ou, J.-H.; Li, H.; Yang, S.-S.; Jiang, A.-M.; Ni, Q.; Liu, J.-L.; Liu, J.-P.; Zheng, H.; Yue-Yang; Ling, R.; He, J.-J.; Li, Z.-G.; Zeng, J.; Zou, T.-N.; Jiang, J.; Song, Z.-J.; Liu, Q.-L.; Ren, G.-S. 2018: Clinicopathologic Factors Related to the Histological Tumor Grade of Breast Cancer in Western China: An Epidemiological Multicenter Study of 8619 Female Patients. Translational Oncology 11(4): 1023-1033

Shepard, R.K.; Ellenbogen, K.A. 2009: Predicting outcome after implantable cardioverter-defibrillator therapy: a new piece to the puzzle?. Journal of the American College of Cardiology 54(9): 829-831

Sofijanova, A.; Piperkova, K.; Al Khalili, D. 2012: Predicting outcome after severe brain injury in risk neonates using the serum S100B biomarker: results using single (24 h) time-point. Prilozi 33(1): 147-156

Berger, R.P. 2011: Predicting outcome after severe pediatric traumatic brain injury: making progress one baby step at a time. Pediatric Critical Care Medicine: a Journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies 12(3): 362-364

Rainey, T.; Lesko, M.; Sacho, R.; Lecky, F.; Childs, C. 2009: Predicting outcome after severe traumatic brain injury using the serum S100B biomarker: results using a single (24h) time-point. Resuscitation 80(3): 341-345

Gialanella, B.; Bertolinelli, M.; Lissi, M.; Prometti, P. 2011: Predicting outcome after stroke: the role of aphasia. Disability and Rehabilitation 33(2): 122-129

Gialanella, B.; Santoro, R.; Ferlucci, C. 2013: Predicting outcome after stroke: the role of basic activities of daily living predicting outcome after stroke. European Journal of Physical and Rehabilitation Medicine 49(5): 629-637

Raj, R.; Siironen, J.; Kivisaari, R.; Hernesniemi, J.; Skrifvars, M.B. 2014: Predicting outcome after traumatic brain injury: development of prognostic scores based on the IMPACT and the APACHE Ii. Journal of Neurotrauma 31(20): 1721-1732

Rimington, H.; Weinman, J.; Chambers, J.B. 2010: Predicting outcome after valve replacement. Heart 96(2): 118-123

Schwab, F.J.; Lafage, V.; Farcy, J.-P.; Bridwell, K.H.; Glassman, S.; Shainline, M.R. 2008: Predicting outcome and complications in the surgical treatment of adult scoliosis. Spine 33(20): 2243-2247

Hope, T.M.H.; Seghier, M.L.; Leff, A.P.; Price, C.J. 2013: Predicting outcome and recovery after stroke with lesions extracted from MRI images. Neuroimage. Clinical 2: 424-433

Park, J.O.; Qin, L.-X.; Prete, F.P.; Antonescu, C.; Brennan, M.F.; Singer, S. 2009: Predicting outcome by growth rate of locally recurrent retroperitoneal liposarcoma: the one centimeter per month rule. Annals of Surgery 250(6): 977-982

De Ruysscher, D. 2013: Predicting outcome by images?. Clinical Cancer Research: An Official Journal of the American Association for Cancer Research 19(13): 3334-3336

Farooq, N.; Patterson, A.J.; Walsh, S.R.; Prytherch, D.R.; Justin, T.A.; Tang, T.Y. 2011: Predicting outcome following colorectal cancer surgery using a colorectal biochemical and haematological outcome model (Colorectal BHOM). Colorectal Disease: the Official Journal of the Association of Coloproctology of Great Britain and Ireland 13(11): 1237-1241

Grant, N.; Hotopf, M.; Breen, G.; Cleare, A.; Grey, N.; Hepgul, N.; King, S.; Moran, P.; Pariante, C.M.; Wingrove, J.; Young, A.H.; Tylee, A.é 2014: Predicting outcome following psychological therapy in IAPT (PROMPT): a naturalistic project protocol. Bmc Psychiatry 14: 170

Kowal, R.C. 2009: Predicting outcome from AF ablation: size of the chamber, or is tissue the issue?. Journal of Cardiovascular Electrophysiology 20(9): 1011-1013

Yacoub, S.; Wills, B. 2014: Predicting outcome from dengue. Bmc Medicine 12: 147

Saubermann, A.J. 2010: Predicting outcome from past performance. Anesthesiology 112(5): 1065-1066

Bodart, O.; Laureys, S. 2014: Predicting outcome from subacute unresponsive wakefulness syndrome or vegetative state. Critical Care 18(2): 132

van Bragt, P.J.; van Ginneken, B.T.; Westendorp, T.; Heijenbrok-Kal, M.H.; Wijffels, M.P.; Ribbers, G.M. 2014: Predicting outcome in a postacute stroke rehabilitation programme. International Journal of Rehabilitation Research. Internationale Zeitschrift für Rehabilitationsforschung. Revue Internationale de Recherches de Readaptation 37(2): 110-117

MacQuillan, G. 2007: Predicting outcome in acute liver failure: are we there yet?. Liver Transplantation: Official Publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society 13(9): 1209-1211

Wand, B.M.; McAuley, J.H.; Marston, L.; De Souza, L.H. 2009: Predicting outcome in acute low back pain using different models of patient profiling. Spine 34(18): 1970-1975

Singer, J.; Gilbert, J.R.; Hutton, T.; Taylor, D.W. 1987: Predicting outcome in acute low-back pain. Canadian Family Physician Medecin de Famille Canadien 33: 655-659

Pradeep, D.J.; Keat, A.; Gaffney, K. 2008: Predicting outcome in ankylosing spondylitis. Rheumatology 47(7): 942-945

Pellikka, P.A. 2010: Predicting outcome in asymptomatic aortic stenosis: should we measure the severity of obstruction or its physiological consequences?. European Heart Journal 31(18): 2191-2193

Catchpoole, D.; Guo, D.; Jiang, H.; Biesheuvel, C. 2008: Predicting outcome in childhood acute lymphoblastic leukemia using gene expression profiling: prognostication or protocol selection?. Blood 111(4): 2486-7; author reply 2487-8

Abend, N.S.; Licht, D.J. 2008: Predicting outcome in children with hypoxic ischemic encephalopathy. Pediatric Critical Care Medicine: a Journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies 9(1): 32-39

De Graaf, L.E.; Hollon, S.D.; Huibers, M.J.H. 2010: Predicting outcome in computerized cognitive behavioral therapy for depression in primary care: a randomized trial. Journal of Consulting and Clinical Psychology 78(2): 184-189

Kahn, J.M. 2014: Predicting outcome in critical care: past, present and future. Current Opinion in Critical Care 20(5): 542-543

LeBrun, D.; Baetz, T.; Foster, C.; Farmer, P.; Sidhu, R.; Guo, H.; Harrison, K.; Somogyi, R.; Greller, L.D.; Feilotter, H. 2008: Predicting outcome in follicular lymphoma by using interactive gene pairs. Clinical Cancer Research: An Official Journal of the American Association for Cancer Research 14(2): 478-487

Jennings, L.; Murphy, G.M. 2009: Predicting outcome in melanoma: where are we now?. British Journal of Dermatology 161(3): 496-503

Lonie, J.A.; Parra-Rodriguez, M.A.; Tierney, K.M.; Herrmann, L.L.; Donaghey, C.; O'Carroll, R.E.; Ebmeier, K.P. 2010: Predicting outcome in mild cognitive impairment: 4-year follow-up study. British Journal of Psychiatry: the Journal of Mental Science 197(2): 135-140

Pitts, W.R. 2008: Predicting outcome in minimally invasive (T1a and T1b) urothelial bladder cancer using a panel of biomarkers: a high-throughput tissue microarray analysis. Bju International 101(12): 1595

Lin, H-Jia.; Ma, X-Lu.; Shi, L-Ping.; Luo, F.; Du, L-Zhong. 2013: Predicting outcome in necrotizing enterocolitis with the score for neonatal acute physiology: a retrospective study of 62 cases. Zhonghua Er Ke Za Zhi 51(5): 326-330

Sinclair, H.; Paterson, M.; Walker, S.; Beckett, G.; Fox, K.A.A. 2007: Predicting outcome in patients with acute coronary syndrome: evaluation of B-type natriuretic peptide and the global registry of acute coronary events (GRACE) risk score. Scottish Medical Journal 52(3): 8-13

Harbrecht, B.G. 2012: Predicting outcome in patients with acute liver failure: what works best?. Critical Care Medicine 40(5): 1666-1667

Coglianese, E.E.; Davidoff, R. 2009: Predicting outcome in patients with asymptomatic aortic stenosis. Circulation 120(1): 9-11

Gane, E. 2008: Predicting outcome in patients with cirrhosis following acute decompensation: can we do better?. Journal of Gastroenterology and Hepatology 23(8 Part 1): 1163-1165

Charles, P-Emmanuel.; Gibot, Sébastien. 2014: Predicting outcome in patients with sepsis: new biomarkers for old expectations. Critical Care 18(1): 108

Ross, D.S. 2013: Predicting outcome in patients with thyroid cancer. Journal of Clinical Endocrinology and Metabolism 98(12): 4673-4675

Zalunardo, N. 2008: Predicting outcome in peritoneal dialysis-related peritonitis: revisiting old themes and slowly moving forward. Peritoneal Dialysis International: Journal of the International Society for Peritoneal Dialysis 28(4): 335-339

Lammers, W.J.; Kowdley, K.V.; van Buuren, H.R. 2014: Predicting outcome in primary biliary cirrhosis. Annals of Hepatology 13(4): 316-326

Doughty, R.N.; Poppe, K.; Stewart, J. 2007: Predicting outcome in severe heart failure. who will benefit from device therapy (CRT)?. European Heart Journal 28(15): 1790-1792

Sobuwa, S.; Hartzenberg, H.B.; Geduld, H.; Uys, C. 2014: Predicting outcome in severe traumatic brain injury using a simple prognostic model. South African Medical Journal 104(7): 492-494

Mancini, G.B.J.; Hartigan, P.M.; Shaw, L.J.; Berman, D.S.; Hayes, S.W.; Bates, E.R.; Maron, D.J.; Teo, K.; Sedlis, S.P.; Chaitman, B.R.; Weintraub, W.S.; Spertus, J.A.; Kostuk, W.J.; Dada, M.; Booth, D.C.; Boden, W.E. 2014: Predicting outcome in the COURAGE trial (Clinical Outcomes Utilizing Revascularization and Aggressive Drug Evaluation): coronary anatomy versus ischemia. JACC. Cardiovascular Interventions 7(2): 195-201

Zafonte, R.D.; Hammond, F.M.; Peterson, J. 1996: Predicting outcome in the slow to respond traumatically brain-injured patient: Acute and subacute parameters. Neurorehabilitation 6(1): 19-32

Voorhies, R.M.; Jiang, X.; Thomas, N. 2007: Predicting outcome in the surgical treatment of lumbar radiculopathy using the Pain Drawing Score, McGill Short Form Pain Questionnaire, and risk factors including psychosocial issues and axial joint pain. Spine Journal: Official Journal of the North American Spine Society 7(5): 516-524

Raj, R.; Siironen, J.; Skrifvars, M.B.; Hernesniemi, J.; Kivisaari, R. 2014: Predicting outcome in traumatic brain injury: development of a novel computerized tomography classification system (Helsinki computerized tomography score). Neurosurgery 75(6): 632

Kent, T.A. 2012: Predicting outcome of IV thrombolysis-treated ischemic stroke patients: the DRAGON score. Neurology 78(17): 1368

Bruno, A.; Switzer, J.A. 2012: Predicting outcome of IV thrombolysis-treated ischemic stroke patients: the DRAGON score. Neurology 79(5): 486-7; author reply 486-7

Warmerdam, L.; Van Straten, A.; Twisk, J.; Cuijpers, P. 2013: Predicting outcome of Internet-based treatment for depressive symptoms. PsychoTherapy Research: Journal of the Society for PsychoTherapy Research 23(5): 559-567

Brugha, T.S.; Taub, N.; Smith, J.; Morgan, Z.; Hill, T.; Meltzer, H.; Wright, C.; Burns, T.; Priebe, S.; Evans, J.; Fryers, T. 2012: Predicting outcome of assertive outreach across England. Social Psychiatry and Psychiatric Epidemiology 47(2): 313-322

Van Noorden, M.S.; van Fenema, E.M.; van der Wee, N.J.A.; Zitman, F.G.; Giltay, E.J. 2012: Predicting outcome of depression using the depressive symptom profile: the Leiden Routine Outcome Monitoring Study. Depression and Anxiety 29(6): 523-530

Mehta, J.P.; Ajwani, V.R.; Singh, V.K. 2009: Predicting outcome of endoscopic management of urethral strictures: guideline for improving results. International Surgery 94(2): 141-143

Sakurada, S.; Watanabe, Y.; Tokunaga, H.; Takahashi, F.; Yamada, H.; Takehara, K.; Yaegashi, N. 2018: Clinicopathologic features and BRCA mutations in primary fallopian tube cancer in Japanese women. Japanese Journal of Clinical Oncology 48(9): 794-798

Kohler, M.; Bloch, K.E. 2008: Predicting outcome of nasal surgery in patients with obstructive sleep apnoea. European Respiratory Journal 32(1): 246; Author Reply 246-7

Murakami, T.; Hayasaka, S.; Terada, Y.; Yuki, H.; Tamura, M.; Yokomizo, R.; Nabeshima, H.; Yaegashi, N.; Okamura, K. 2008: Predicting outcome of one-step total hysteroscopic resection of sessile submucous myoma. Journal of Minimally Invasive Gynecology 15(1): 74-77

Mudd, K.; Paterakis, M.; Curtin-Brosnan, J.; Matsui, E.; Wood, R. 2009: Predicting outcome of repeat milk, egg, or peanut oral food challenges. Journal of Allergy and Clinical Immunology 124(5): 1115-1116

Boschen, M.J.; Drummond, L.M.; Pillay, A.; Morton, K. 2010: Predicting outcome of treatment for severe, treatment resistant OCD in inpatient and community settings. Journal of Behavior Therapy and Experimental Psychiatry 41(2): 90-95

Velickiene, D.; Kazanavicius, G.; Danilevicius, J.; Jankauskiene, J.ūr. 2007: Predicting outcome of treatment with radiotherapy in endocrine ophthalmopathy. Medicina 43(3): 190-198

Syazarina Sharis, O.; Zulkifli, M.Z.; Hamzaini, A.H. 2013: Predicting outcome of trial of voiding without catheter in acute urinary retention with intravesical prostatic protrusion. Malaysian Journal of Medical Sciences: Mjms 20(1): 56-59

Golshayan, A.-R.; Brick, A.J.; Choueiri, T.K. 2008: Predicting outcome to VEGF-targeted therapy in metastatic clear-cell renal cell carcinoma: data from recent studies. Future Oncology 4(1): 85-92

Milette, F.ço. 2012: Predicting outcome: a fourfold delusion!. Dermatology Practical and Conceptual 2(2): 202a14

Battle, C.; Hutchings, H.; Lovett, S.; Bouamra, O.; Jones, S.; Sen, A.; Gagg, J.; Robinson, D.; Hartford-Beynon, J.; Williams, J.; Evans, A. 2014: Predicting outcomes after blunt chest wall trauma: development and external validation of a new prognostic model. Critical Care 18(3): R98

Choi, J.S.; Kim, C.S.; Bae, E.H.; Ma, S.K.; Ahn, Y.-K.; Jeong, M.H.; Kim, Y.J.; Cho, M.C.; Kim, C.J.; Kim, S.W.; Jeong, M.H.; Ahn, Y.K.; Chae, S.C.; Kim, J.H.; Hur, S.H.; Kim, Y.J.; Seong, W.; Choi, D.H.; Chae, J.K.; Hong, T.J.; Rhew, J.Y.; Kim, D.I.; Chae, H.; Yoon, J.H.; Koo, K.; Kim, B.O.; Lee, M.Y.; Kim, K.S.; Hwang, J.Y.; Cho, M.C.; Kyu, S.; Lee, N.H.; Jeong, K.T.; Tahk, S.J.; Bae, J.H.; Rha, S.W.; Park, K.S.; Kim, C.J.; Han, K.R.; Ahn, T.H.; Kim, M.H.; Seung, K.B.; Chung, W.S.; Yang, J.Y.; Rhim, C.Y.; Gwon, H.C.; Park, S.W.; Koh, Y.Y.; Joo, S.J.; Kim, S.J.; Jin, D.K.; Cho, J.M.; Kim, B.O.; Kim, S.-W.; Kim, J.K.; Kim, T.I.; Nah, D.Y.; Park, S.H.; Lee, S.H.; Lee, S.U.; Chung, H.-J.; Cho, J.H.; Jin, S.W.; Jang, Y.S.; Cho, J.G.; Park, S.J 2012: Predicting outcomes after myocardial infarction by using the Chronic Kidney Disease Epidemiology Collaboration equation in comparison with the Modification of Diet in Renal Disease study equation: results from the Korea Acute Myocardial Infarction Registry. Nephrology Dialysis Transplantation: Official Publication of the European Dialysis and Transplant Association - European Renal Association 27(10): 3868-3874

Papadakis, K.A. 2012: Predicting outcomes after restorative proctocolectomy for ulcerative colitis. Clinical Gastroenterology and Hepatology: the Official Clinical Practice Journal of the American Gastroenterological Association 10(5): 447-449

Rempe, D.A. 2014: Predicting outcomes after transient ischemic attack and stroke. Continuum 20(2 Cerebrovascular Disease): 412-428

Yuan, F.; Ding, J.; Chen, H.; Guo, Y.; Wang, G.; Gao, W.-W.; Chen, S.-W.; Tian, H.-L. 2012: Predicting outcomes after traumatic brain injury: the development and validation of prognostic models based on admission characteristics. Journal of Trauma and Acute Care Surgery 73(1): 137-145

Oruche, U.M.; Gerkensmeyer, J.E.; Carpenter, J.S.; Austin, J.K.; Perkins, S.M.; Rawl, S.M.; Wright, E.R. 2013: Predicting outcomes among adolescents with disruptive disorders being treated in a system of care program. Journal of the American Psychiatric Nurses Association 19(6): 335-344

Dungan, K.M. 2012: Predicting outcomes and assessing control with alternate glycemic markers. Diabetes Technology and Therapeutics 14(9): 749-752

Mohapatra, P.R.; Hari, D.T. 2011: Predicting outcomes and drug resistance with new standardised treatment. European Respiratory Journal 37(4): 974; Author Reply 974-5

Oxlade, O.; Schwartzman, K.; Pai, M.; Heymann, J.; Benedetti, A.; Royce, S.; Menzies, D. 2010: Predicting outcomes and drug resistance with standardised treatment of active tuberculosis. European Respiratory Journal 36(4): 870-877

Steer, J.; Gibson, G.J.; Bourke, S.C. 2010: Predicting outcomes following hospitalization for acute exacerbations of COPD. Qjm: Monthly Journal of the Association of Physicians 103(11): 817-829

Kramer, A.A.; Zimmerman, J.E. 2008: Predicting outcomes for cardiac surgery patients after intensive care unit admission. Seminars in Cardiothoracic and Vascular Anesthesia 12(3): 175-183

Ling, S.C.; Avitzur, Y. 2014: Predicting outcomes for children awaiting liver transplantation: is serum sodium the answer?. Hepatology 59(5): 1678-1680

Vermeulen, J.ël.; De Preter, K.; Mestdagh, P.; Laureys, G.èv.; Speleman, F.; Vandesompele, J. 2010: Predicting outcomes for children with neuroblastoma. Discovery Medicine 10(50): 29-36

Vermeulen, Jëlle.; De Preter, K.; Naranjo, A.; Vercruysse, L.; Van Roy, N.; Hellemans, J.; Swerts, K.; Bravo, S.; Scaruffi, P.; Tonini, G.Paolo.; De Bernardi, B.; Noguera, R.; Piqueras, M.; Cañete, A.; Castel, V.; Janoueix-Lerosey, I.; Delattre, O.; Schleiermacher, G.; Michon, J.; Combaret, Vérie.; Fischer, M.; Oberthuer, Aé.; Ambros, P.F.; Beiske, K.; Bénard, J.; Marques, Bárbara.; Rubie, Hé.; Kohler, J.; Pötschger, U.; Ladenstein, R.; Hogarty, M.D.; McGrady, P.; London, W.B.; Laureys, Gève.; Speleman, F.; Vandesompele, J. 2009: Predicting outcomes for children with neuroblastoma using a multigene-expression signature: a retrospective SIOPEN/COG/GPOH study. Lancet. Oncology 10(7): 663-671

Schubert, C.A.; Mulvey, E.P.; Loughran, T.A.; Fagan, J.; Chassin, L.A.; Piquero, A.R.; Losoya, S.H.; Steinberg, L.; Cauffman, E. 2010: Predicting outcomes for youth transferred to adult court. Law and Human Behavior 34(6): 460-475

Kurth, T.; Glynn, R.J. 2008: Predicting outcomes in CKD. American Journal of Kidney Diseases: the Official Journal of the National Kidney Foundation 52(4): 635-637

Levin, A. 2009: Predicting outcomes in CKD: the importance of perspectives, populations and practices. Nephrology Dialysis Transplantation: Official Publication of the European Dialysis and Transplant Association - European Renal Association 24(6): 1724-1726

Grill, S.; Weuve, J.; Weisskopf, M.G. 2011: Predicting outcomes in Parkinson's disease: comparison of simple motor performance measures and the Unified Parkinson's Disease Rating Scale-IIi. Journal of Parkinson's Disease 1(3): 287-298

Dupuis, J.-Y. 2008: Predicting outcomes in cardiac surgery: risk stratification matters?. Current Opinion in Cardiology 23(6): 560-567

Huang, Z.; Mayr, N.A.; Yuh, W.T.C.; Lo, S.S.; Montebello, J.F.; Grecula, J.C.; Lu, L.; Li, K.; Zhang, H.; Gupta, N.; Wang, J.Z. 2010: Predicting outcomes in cervical cancer: a kinetic model of tumor regression during radiation therapy. Cancer Research 70(2): 463-470

Franzen, D.; Rupprecht, C.; Hauri, D.; Bleisch, J.A.; Staubli, M.; Puhan, M.A. 2010: Predicting outcomes in critically ill patients with acute kidney injury undergoing intermittent hemodialysis--a retrospective cohort analysis. International Journal of Artificial Organs 33(1): 15-21

O'Sullivan, E.; Callely, E.; O'Riordan, D.; Bennett, K.; Silke, B. 2012: Predicting outcomes in emergency medical admissions - role of laboratory data and co-morbidity. Acute Medicine 11(2): 59-65

Nowak-Wegrzyn, A. 2012: Predicting outcomes in food challenges: what's the score?. Israel Medical Association Journal: Imaj 14(1): 48-49