{"id":"W3203841805","doi":"10.1093/jamia/ocab196","title":"Gender harmony: improved standards to support affirmative care of gender-marginalized people through inclusive gender and sex representation","year":2021,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Sex and Gender in Healthcare","field":"Medicine","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Health Infoway; University of Victoria","funders":"U.S. National Library of Medicine","keywords":"Harmony (color); Gender diversity; Health care; Interoperability; Gender identity; Computer science; Psychology; Medicine; Political science; Social psychology; Law; Business","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":[],"consensus_categories":[],"category_scores_codex":[0.1343251,0.0008648863,0.0007262069,0.004361001,0.006734232,0.01279979,0.00564626,0.005163911,0.007746611],"category_scores_gemma":[0.1256826,0.0008411754,0.001793593,0.002154362,0.01747569,0.02320566,0.02222934,0.006686508,0.003480564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008987263,"about_ca_system_score_gemma":0.05140672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009674674,"about_ca_topic_score_gemma":0.0077753,"domain_scores_codex":[0.898757,0.06243138,0.009010604,0.006040647,0.02060576,0.003154614],"domain_scores_gemma":[0.9065704,0.03086231,0.008046573,0.01898598,0.03005222,0.005482426],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00009352249,0.00017676,0.004944941,0.0005637816,0.00003423752,0.0001963012,0.02370933,0.001160468,0.001715992,0.7676719,0.05200276,0.14773],"study_design_scores_gemma":[0.00008607911,0.0003111861,0.003655625,0.002524264,0.0000679212,0.0004862902,0.0177074,0.00401147,0.004979124,0.3792019,0.5867792,0.0001895447],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02137269,0.002144475,0.7272899,0.1147223,0.003669473,0.002440721,0.0009092311,0.002365988,0.1250853],"genre_scores_gemma":[0.2505807,0.002311845,0.6842817,0.03099754,0.001327105,0.003999189,0.002038024,0.001008901,0.02345494],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1343251,"threshold_uncertainty_score":0.7103873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03646101102386387,"score_gpt":0.3864630659106519,"score_spread":0.350002054886788,"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."}}