{"id":"W2990791220","doi":"10.1017/s003329171900326x","title":"The impact of psychiatric and medical comorbidity on the risk of mortality: a population-based analysis","year":2019,"lang":"en","type":"article","venue":"Psychological Medicine","topic":"Chronic Disease Management Strategies","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Institute for Work & Health; Institute for Clinical Evaluative Sciences; Centre for Addiction and Mental Health","funders":"","keywords":"Comorbidity; Medicine; Hazard ratio; Population; Psychiatry; Major depressive disorder; Cohort; Pediatrics; Internal medicine; Confidence interval","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001450728,0.0001640717,0.0005684428,0.0001557828,0.00006780015,0.000005712497,0.0002224542,0.00009032286,0.00267883],"category_scores_gemma":[0.001245314,0.00006136103,0.0002918363,0.0009399683,0.0004848104,0.00001507686,0.00003300401,0.0002905653,0.000007330307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003327234,"about_ca_system_score_gemma":0.00002719844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009350816,"about_ca_topic_score_gemma":0.00003594067,"domain_scores_codex":[0.9979116,0.0002486569,0.0005081791,0.0002856578,0.0008547791,0.0001910803],"domain_scores_gemma":[0.9972753,0.001388428,0.0003312603,0.0007773668,0.00007098146,0.0001566563],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004278069,0.0004151741,0.9886438,0.00005171564,0.001249618,0.000005247612,0.00002388944,0.0001284359,0.00001199135,0.003535138,0.002149707,0.003357431],"study_design_scores_gemma":[0.001889083,0.001625184,0.988387,0.00006970048,0.001450614,0.000001743589,0.000105862,0.004262744,0.000001499945,0.002110086,0.00003882413,0.00005760519],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9844162,0.0004614267,0.0001009302,0.006762408,0.0001201682,0.0005347445,0.00001861825,0.00002259112,0.007562889],"genre_scores_gemma":[0.9988911,0.0004345354,0.00002046095,0.0004627254,0.0000952573,0.00001233648,0.00003595262,0.000006765437,0.00004092769],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01447481,"threshold_uncertainty_score":0.9982328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05478755582770371,"score_gpt":0.4175082905703737,"score_spread":0.36272073474267,"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."}}