{"id":"W4225158835","doi":"10.23889/ijpds.v7i1.1700","title":"A more accurate approach to define abortion cohorts using linked administrative data: an application to Ontario, Canada","year":2022,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Reproductive Health and Contraception","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; McMaster University; Women's College Hospital; Hamilton Health Sciences; University of Toronto; Institute for Clinical Evaluative Sciences; University of British Columbia","funders":"Department of Family and Community Medicine, University of Toronto; Canadian Institutes of Health Research; Ministry of Health, British Columbia; Provincial Health Services Authority; Ontario Ministry of Health and Long-Term Care; University of Toronto; Michael Smith Health Research BC; Public Health Agency; Hypertension Canada; Public Health Agency of Canada","keywords":"Abortion; Medicine; Incidence (geometry); Pregnancy; Obstetrics; Confidence interval; Population; Cohort; Cohort study; Demography; Gynecology; Environmental health; Internal medicine","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":[0.01419989,0.0003975351,0.0005247282,0.002207168,0.002124494,0.001932511,0.001610341,0.0005377224,0.001398466],"category_scores_gemma":[0.04157743,0.0002773426,0.001257321,0.005109071,0.0006195073,0.0005023209,0.00189603,0.001076074,0.000147284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03475527,"about_ca_system_score_gemma":0.04710846,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9857049,"about_ca_topic_score_gemma":0.9835883,"domain_scores_codex":[0.9920358,0.002928369,0.000715985,0.001092499,0.002491908,0.0007355065],"domain_scores_gemma":[0.9764202,0.007337614,0.002869619,0.00245259,0.01006892,0.0008510195],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002470703,0.00008777135,0.9013379,0.0003284782,0.0005374837,0.0002306985,0.001769613,0.02574812,0.0005731282,0.00579907,0.01073819,0.0526023],"study_design_scores_gemma":[0.000365108,0.0001121816,0.8501967,0.0003703405,0.0003555547,0.0001841438,0.001817816,0.1165686,0.0008550195,0.003847252,0.02523212,0.00009513036],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7567952,0.002787245,0.1516564,0.01006659,0.0002260961,0.0042165,0.06064033,0.0007446345,0.01286703],"genre_scores_gemma":[0.8242161,0.0006941406,0.1612592,0.0008420467,0.00005815321,0.0008482836,0.01056796,0.00007562933,0.001438422],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03475527,"threshold_uncertainty_score":0.2521684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1853295254865522,"score_gpt":0.4563042791461537,"score_spread":0.2709747536596016,"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."}}