{"id":"W2513410927","doi":"10.1016/j.contraception.2015.06.075","title":"Accuracy of surgical abortion data capture in Canadian Government Health Administration Databases","year":2015,"lang":"en","type":"article","venue":"Contraception","topic":"Census and Population Estimation","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Medicine; Abortion; Population; Clinical trial; Reproductive health; Coding (social sciences); Medical record; Database; Medical emergency; Environmental health; Pregnancy; Statistics; Surgery","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.008211631,0.0006085856,0.0009164918,0.01073446,0.002757786,0.004227998,0.003153441,0.0009381066,0.003940641],"category_scores_gemma":[0.1059303,0.0008242233,0.0009953594,0.02733717,0.0008451,0.001412863,0.001832624,0.001156625,0.001054031],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04697637,"about_ca_system_score_gemma":0.07812927,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9953988,"about_ca_topic_score_gemma":0.9958496,"domain_scores_codex":[0.9824032,0.002082876,0.001804324,0.001819536,0.009798954,0.00209112],"domain_scores_gemma":[0.9319322,0.01443164,0.007051324,0.004960866,0.04031552,0.001308459],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002146382,0.00005194066,0.8427394,0.0006634371,0.0004827926,0.00009259741,0.001389765,0.003905357,0.0002729824,0.006931067,0.0849093,0.05834671],"study_design_scores_gemma":[0.0000295461,0.00001279764,0.9395306,0.0006048921,0.0002039813,0.00009093778,0.001482285,0.01055597,0.0006776748,0.0007609003,0.04596733,0.00008303734],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.3797172,0.007547157,0.007467821,0.007861817,0.0003861044,0.0004959041,0.566157,0.0006198589,0.02974714],"genre_scores_gemma":[0.8107464,0.003214823,0.007444702,0.0008210313,0.0000710398,0.0001979305,0.1730849,0.00009856242,0.004320633],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9917884,"threshold_uncertainty_score":0.340839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1801915940743374,"score_gpt":0.4157600104456439,"score_spread":0.2355684163713065,"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."}}