{"id":"W3027199967","doi":"10.1177/0706743720927812","title":"Mood Disorders in Late Life: A Population-based Analysis of Prevalence, Risk Factors, and Consequences in Community-dwelling Older Adults in Ontario: Troubles de l’humeur en âge avancé : Une analyse dans la population de la prévalence, des facteurs de risque et des conséquences chez des adultes âgés vivant en milieu communautaire en Ontario","year":2020,"lang":"en","type":"article","venue":"The Canadian Journal of Psychiatry","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital; Institute for Clinical Evaluative Sciences; Centre for Addiction and Mental Health; Public Health Ontario; University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"Mood; Mood disorders; Population; Medicine; Odds ratio; Psychiatry; Comorbidity; National Comorbidity Survey; Gerontology; Psychological intervention; Demography; Environmental health; Anxiety; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"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":[0.0003685164,0.0002599853,0.0003050466,0.0008740679,0.00121915,0.0006872457,0.0006012815,0.0003165006,0.0008030575],"category_scores_gemma":[0.000860799,0.0002668407,0.0005586117,0.001852761,0.000400682,0.0003117155,0.0007354075,0.0003033552,0.0001165563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007215452,"about_ca_system_score_gemma":0.005572662,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9427276,"about_ca_topic_score_gemma":0.9678826,"domain_scores_codex":[0.9996439,0.00002933626,0.00003502005,0.00005576679,0.0001417389,0.00009422981],"domain_scores_gemma":[0.9991649,0.00002998864,0.0002815347,0.00002946672,0.0002890945,0.0002049805],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003063526,0.00001430815,0.9981299,0.00002517881,0.00003094555,0.00004319893,0.0003906239,0.0000267141,0.0001531395,0.000009869863,0.0002116098,0.0009338522],"study_design_scores_gemma":[0.000002544985,0.00001218618,0.9994435,0.000007984288,0.000006750956,0.00002029578,0.0002938805,0.00005031823,0.000009022395,0.000002896564,0.0001489256,0.000001671081],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966173,0.0003612227,0.00009393035,0.00009175104,0.000003632473,0.00004702292,0.002243815,0.000003972839,0.000537346],"genre_scores_gemma":[0.9980069,0.0002654727,0.0001169156,0.00004826846,0.000004525914,0.00003131237,0.001136577,0.000001466856,0.0003885182],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05727237,"threshold_uncertainty_score":0.1152193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01411836971637535,"score_gpt":0.285167041440286,"score_spread":0.2710486717239106,"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."}}