{"id":"W3118447500","doi":"10.1111/dar.13235","title":"Using administrative health data to estimate prevalence and mortality rates of alcohol and other substance‐related disorders for surveillance purposes","year":2021,"lang":"en","type":"article","venue":"Drug and Alcohol Review","topic":"Substance Abuse Treatment and Outcomes","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Douglas Mental Health University Institute; McGill University; Centre Hospitalier de l’Université de Montréal; Université de Montréal; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Dalhousie University; Institut National de Santé Publique du Québec","funders":"","keywords":"Medicine; Mortality rate; Epidemiology; Prevalence; Population; Demography; Environmental health; Psychological intervention; Public health; Disease surveillance; Psychiatry; Surgery; Internal medicine; Pathology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006296653,0.0005807542,0.0004437332,0.007827875,0.0006347836,0.001590355,0.001416881,0.0004159783,0.001558752],"category_scores_gemma":[0.02083927,0.0003059474,0.0006667497,0.01005012,0.000291571,0.0007217526,0.0007860445,0.0004265495,0.0006254677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003712103,"about_ca_system_score_gemma":0.006258637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4870949,"about_ca_topic_score_gemma":0.4175457,"domain_scores_codex":[0.9937326,0.002157097,0.0008530926,0.0006882028,0.002239767,0.0003291959],"domain_scores_gemma":[0.9783893,0.004291271,0.00842347,0.001683356,0.006677617,0.0005349833],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003823718,0.00006024784,0.9459842,0.0005028222,0.0005444222,0.00002989963,0.0001498244,0.001199152,0.0001801379,0.0005476779,0.01644018,0.03432312],"study_design_scores_gemma":[0.00002766245,0.0000305219,0.9855805,0.000363387,0.000117768,0.00006285064,0.0002414429,0.003912136,0.0003843672,0.0003175309,0.008937862,0.00002399496],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.4566638,0.008238342,0.03239776,0.004736986,0.0003138522,0.002132937,0.464864,0.0009987598,0.02965364],"genre_scores_gemma":[0.7882017,0.003939935,0.02797555,0.001202722,0.0002765562,0.001748537,0.1748192,0.00005021077,0.001785599],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4870949,"threshold_uncertainty_score":0.9685194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.240883309196549,"score_gpt":0.4821271307446676,"score_spread":0.2412438215481187,"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."}}