{"id":"W3197451302","doi":"10.1093/jamia/ocab136","title":"Transgender data collection in the electronic health record: Current concepts and issues","year":2021,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"LGBTQ Health, Identity, and Policy","field":"Psychology","cited_by":193,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of Victoria; University of Toronto","funders":"National Institute on Drug Abuse; Agency for Healthcare Research and Quality","keywords":"Transgender; Disclaimer; Terminology; Transgender Person; Health care; Medicine; Leverage (statistics); Data collection; Internet privacy; Psychology; Family medicine; Medical education; Computer science; Sociology; Political science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.3435183,0.0007882043,0.001579096,0.009641876,0.008703914,0.0256567,0.009258175,0.005939191,0.004657864],"category_scores_gemma":[0.4238916,0.001643714,0.001679858,0.01223428,0.03919742,0.04658966,0.0170789,0.01004566,0.002500408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01091949,"about_ca_system_score_gemma":0.02744394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01465061,"about_ca_topic_score_gemma":0.0132178,"domain_scores_codex":[0.6466373,0.2618441,0.03349285,0.01390872,0.04071149,0.003405685],"domain_scores_gemma":[0.3159235,0.5198091,0.03187351,0.04981045,0.07539779,0.007185757],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000263187,0.0002625202,0.03932945,0.007723209,0.0001585815,0.00041921,0.07637561,0.0004172005,0.001194412,0.1703861,0.0550584,0.6484122],"study_design_scores_gemma":[0.00007660776,0.0003651184,0.02612566,0.04466599,0.000246688,0.003112209,0.1759678,0.002092515,0.003031245,0.2358984,0.5078607,0.0005570676],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.02337741,0.07045032,0.1043368,0.7707672,0.005497366,0.0009742117,0.001092653,0.0005083648,0.02299561],"genre_scores_gemma":[0.31348,0.1188709,0.3521225,0.189672,0.01360547,0.003582688,0.001709629,0.0009339527,0.006022739],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3435183,"threshold_uncertainty_score":0.8095587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04668196917660047,"score_gpt":0.4530278904382472,"score_spread":0.4063459212616467,"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."}}