{"id":"W4214525197","doi":"10.1192/bjp.2022.28","title":"Using polygenic scores and clinical data for bipolar disorder patient stratification and lithium response prediction: machine learning approach","year":2022,"lang":"en","type":"article","venue":"The British Journal of Psychiatry","topic":"Bipolar Disorder and Treatment","field":"Medicine","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for Movement Disorders; McGill University; Douglas Mental Health University Institute; McGill University Health Centre; Dalhousie University; Montreal Neurological Institute and Hospital","funders":"NIH Clinical Center; Agence Nationale de la Recherche; Deutsche Forschungsgemeinschaft; U.S. Department of Veterans Affairs; Canadian Institutes of Health Research; Grantová Agentura České Republiky","keywords":"Bipolar disorder; Major depressive disorder; Population stratification; Schizophrenia (object-oriented programming); Psychology; Lithium (medication); Clinical psychology; Statistics; Machine learning; Artificial intelligence; Psychiatry; Computer science; Cognition; Mathematics; Single-nucleotide polymorphism; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00154435,0.00008675319,0.0002057464,0.00005134298,0.0008541557,0.00008417808,0.0001248488,0.00003865408,0.00001396599],"category_scores_gemma":[0.0001158195,0.0000726345,0.00007338609,0.00009885206,0.0000920957,0.0001085887,0.000117761,0.0005277558,8.863099e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002246799,"about_ca_system_score_gemma":0.0001955196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001235022,"about_ca_topic_score_gemma":0.00008785447,"domain_scores_codex":[0.9985476,0.0004182052,0.0004828266,0.0001979826,0.0002342243,0.0001191219],"domain_scores_gemma":[0.9992386,0.000106171,0.0002835277,0.0002276209,0.00005456474,0.00008957504],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004458076,0.001178848,0.8692517,0.00008027584,0.0005114524,0.000007375494,0.0003047248,0.0001285628,0.00007533707,0.00007964068,0.000658881,0.1232651],"study_design_scores_gemma":[0.005998943,0.004162204,0.9178706,0.0001571575,0.001301737,0.01641703,0.002484366,0.02164526,0.000002362036,0.0005304084,0.02926812,0.0001617562],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7833191,0.2115308,0.00247358,0.001655705,0.000394043,0.000366083,0.0002378889,0.00001001396,0.00001287166],"genre_scores_gemma":[0.983646,0.004721678,0.01092354,0.0001297369,0.0003158714,0.000008174009,0.0001505654,0.00002908744,0.00007529365],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2068091,"threshold_uncertainty_score":0.6569561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04624303011947828,"score_gpt":0.3203143575729224,"score_spread":0.2740713274534441,"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."}}