{"id":"W4296850367","doi":"10.1016/j.euroneuro.2022.07.040","title":"MERGING THE BOUNDARIES BETWEEN THE BRAIN AND THE HEART: UNDERSTANDING THE SHARED BIOLOGY OF MOOD DISORDERS AND CARDIO-METABOLIC PHENOTYPES USING GENETICS, GENOMICS AND ELECTRONIC HEALTH RECORD DATA","year":2022,"lang":"en","type":"article","venue":"European Neuropsychopharmacology","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Phenotype; Genomics; Biology; Health records; Mood disorders; Genetics; Mood; Computational biology; Bioinformatics; Evolutionary biology; Psychology; Genome; Psychiatry; Gene; Health care","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":["sts"],"category_scores_codex":[0.004162327,0.0002080969,0.00028882,0.00003736972,0.002542589,0.0000890318,0.0007678179,0.00001819859,0.00005557084],"category_scores_gemma":[0.00008984444,0.0001211717,0.00003559751,0.0002312591,0.003231864,0.00008770033,0.002156411,0.0007025828,0.000002468232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001094398,"about_ca_system_score_gemma":0.00006063065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003763889,"about_ca_topic_score_gemma":0.0001858597,"domain_scores_codex":[0.9924024,0.005835032,0.0003764521,0.000663063,0.0001710454,0.0005520429],"domain_scores_gemma":[0.9978784,0.001149986,0.0002801532,0.0006097855,0.000003287971,0.0000784435],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00168574,0.0002283154,0.5121193,0.0001297615,0.001496782,0.00001326546,0.06350119,0.008687592,0.08605222,0.007430379,0.007267953,0.3113875],"study_design_scores_gemma":[0.00360057,0.0004097853,0.5320853,0.000006768334,0.0003075287,0.0001080665,0.003948406,0.01084617,0.00002013761,0.004072866,0.4442006,0.0003937528],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9546115,0.005645499,0.001220173,0.03665937,0.0002845865,0.001011976,0.00009594532,0.00002365539,0.0004472875],"genre_scores_gemma":[0.988793,0.005473236,0.00007119714,0.005459538,0.0001123457,0.00001253275,0.000009345793,0.00005859215,0.00001025456],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4369327,"threshold_uncertainty_score":0.9994808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03798818540710847,"score_gpt":0.303590697859233,"score_spread":0.2656025124521245,"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."}}