{"id":"W2972265869","doi":"10.1093/ije/dyz173","title":"Cohort Profile: The Canadian Longitudinal Study on Aging (CLSA)","year":2019,"lang":"en","type":"article","venue":"International Journal of Epidemiology","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":532,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Bruyère; Centres Intégré Universitaires de Santé et de Services Sociaux; University of Manitoba; University of British Columbia; Alberta Health; Dalhousie University; Simon Fraser University; University of Ottawa; McGill University; McGill University Health Centre; Alberta Health Services; St. John’s Health Sciences Centre; Université de Sherbrooke; McMaster University; University of Calgary; University of Victoria; Impact","funders":"Ontario Ministry of Transportation; Canadian Institutes of Health Research; Ontario Ministry of Health and Long-Term Care; Canada Foundation for Innovation","keywords":"Cohort; Cohort study; Medicine; Gerontology; Longitudinal study; Demography; Internal medicine; Pathology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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":[0.003085319,0.0009108129,0.001543005,0.006399681,0.002404912,0.001598595,0.002647263,0.0007033257,0.02643922],"category_scores_gemma":[0.01150863,0.0004134552,0.001752262,0.01062689,0.0003374056,0.00095722,0.001755323,0.001325801,0.003857041],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01127559,"about_ca_system_score_gemma":0.04601437,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9661525,"about_ca_topic_score_gemma":0.9786115,"domain_scores_codex":[0.9983122,0.0001364013,0.0002645719,0.0002018408,0.0007448889,0.0003401235],"domain_scores_gemma":[0.9944407,0.000185367,0.000410214,0.0002466847,0.004138698,0.0005783588],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.001061212,0.0001603742,0.3568143,0.002836285,0.0009838052,0.0001694265,0.0004274206,0.0004709332,0.0002607304,0.003349467,0.593186,0.04028013],"study_design_scores_gemma":[0.0003298919,0.00006797913,0.9224511,0.0009182646,0.0004480474,0.00008824731,0.000523323,0.0005284235,0.0001430559,0.0005817115,0.07384868,0.00007132445],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.02391409,0.001354073,0.0007027435,0.001189278,0.0002123753,0.001461412,0.9583512,0.0002113737,0.01260343],"genre_scores_gemma":[0.1376791,0.004050859,0.005710051,0.0008079289,0.0001542449,0.005122207,0.8169901,0.0002266977,0.02925875],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9887244,"threshold_uncertainty_score":0.08844799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1646518248003988,"score_gpt":0.4826248311374556,"score_spread":0.3179730063370567,"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."}}