{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003402286,0.0002823494,0.0005707662,0.003298742,0.0005058443,0.00442169,0.0009447127,0.0009861036,0.001504395],"category_scores_gemma":[0.01379277,0.0003175887,0.0004527492,0.003271856,0.001106169,0.004833583,0.002693992,0.001337906,0.000259306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004667811,"about_ca_system_score_gemma":0.001052571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007918229,"about_ca_topic_score_gemma":0.01383127,"domain_scores_codex":[0.9979209,0.0009834154,0.0002152647,0.0004046659,0.0003216904,0.0001539746],"domain_scores_gemma":[0.9915547,0.00436403,0.002074635,0.0009790697,0.0007433479,0.0002841899],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004737175,0.0001853341,0.6824041,0.0005895857,0.0008943462,0.0009928739,0.00716078,0.002231297,0.008984008,0.02781421,0.006475179,0.2617945],"study_design_scores_gemma":[0.00004995262,0.0001460797,0.8364782,0.0009855918,0.0005982901,0.001270414,0.007602591,0.008724043,0.003077803,0.1160402,0.02490718,0.0001196982],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8689373,0.01664384,0.06654662,0.02589774,0.0003611898,0.00007883248,0.006441186,0.0002344383,0.01485886],"genre_scores_gemma":[0.9691404,0.00389103,0.02272992,0.001857269,0.0002271065,0.0000355189,0.001576791,0.00006411945,0.0004777924],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007918229,"threshold_uncertainty_score":0.01799327,"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."}}