{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001689355,0.0001101384,0.0001552393,0.0002617005,0.0007316887,0.0001514293,0.00123661,0.00001960465,0.00003619675],"category_scores_gemma":[0.0005470447,0.0001084267,0.000016918,0.0004809688,0.00003192013,0.001729384,0.0004162039,0.0002348227,0.000001012111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00200069,"about_ca_system_score_gemma":0.003418806,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.4069275,"about_ca_topic_score_gemma":0.3416703,"domain_scores_codex":[0.9970493,0.00004015144,0.0004847326,0.0007799966,0.00140296,0.0002429163],"domain_scores_gemma":[0.9976068,0.00002117552,0.0003148705,0.0008643672,0.0008157653,0.0003770929],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.009172689,0.002481522,0.4443073,0.00008729712,0.0003297225,0.00008452676,0.0053017,0.2843776,0.05710078,0.007296224,0.02900474,0.160456],"study_design_scores_gemma":[0.0006911009,0.0002837815,0.7022182,0.00002276129,0.00004882588,0.0006361441,0.0006899002,0.2489431,0.00005536688,0.0001394507,0.04607083,0.0002005085],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8592834,0.00001259332,0.1322818,0.00444344,0.001450214,0.001370459,0.001081064,0.00001670184,0.0000603871],"genre_scores_gemma":[0.9670749,0.000001651744,0.02336107,0.001515255,0.0006705009,0.00007731811,0.007189561,0.00001066548,0.00009909044],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2579109,"threshold_uncertainty_score":0.6703425,"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."}}