{"id":"W4402547692","doi":"10.1016/j.jad.2024.09.025","title":"Machine learning with multiple modalities of brain magnetic resonance imaging data to identify the presence of bipolar disorder","year":2024,"lang":"en","type":"article","venue":"Journal of Affective Disorders","topic":"Bipolar Disorder and Treatment","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Center for Advancing Translational Sciences; U.S. Department of Veterans Affairs; U.S. National Library of Medicine; National Center for Research Resources; National Institute of Dental and Craniofacial Research; National Institute on Drug Abuse; National Institute of Neurological Disorders and Stroke; National Heart, Lung, and Blood Institute; National Institute of Mental Health; NIH Office of the Director; National Institutes of Health; National Institute of Biomedical Imaging and Bioengineering; Roy J. Carver Charitable Trust","keywords":"Bipolar disorder; Magnetic resonance imaging; Neuroimaging; Modalities; Psychology; Neuroscience; Nuclear magnetic resonance; Medicine; Physics; Cognition; Radiology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00257395,0.0007631072,0.0009781804,0.002664424,0.0004175295,0.00125451,0.0006420901,0.0008914613,0.001071053],"category_scores_gemma":[0.007060438,0.0002348457,0.001063521,0.001228535,0.0002973639,0.0009017505,0.0008385786,0.001247936,0.0004638156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003326715,"about_ca_system_score_gemma":0.0006471382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003481368,"about_ca_topic_score_gemma":0.004199651,"domain_scores_codex":[0.9989605,0.0004477798,0.0001154909,0.0002175074,0.0001331052,0.0001255077],"domain_scores_gemma":[0.9968228,0.002277025,0.0002928942,0.0001796978,0.000300726,0.0001267633],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00209363,0.001915969,0.4428708,0.0002438606,0.002124732,0.0005739264,0.0002101607,0.05488264,0.008422321,0.0007342958,0.00741719,0.4785105],"study_design_scores_gemma":[0.00008119967,0.0004304611,0.07783778,0.00009138505,0.0003248962,0.0004188761,0.000262009,0.9133192,0.00242556,0.003674373,0.001091541,0.00004270976],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9375136,0.002595599,0.0515374,0.001661529,0.0002866218,0.0002003224,0.002496409,0.0005439574,0.00316453],"genre_scores_gemma":[0.9833537,0.0003320642,0.0137805,0.0001503713,0.0001694752,0.000058523,0.001737212,0.00001434916,0.0004037155],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003481368,"threshold_uncertainty_score":0.01361251,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01287597769573448,"score_gpt":0.2970527016697567,"score_spread":0.2841767239740223,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}