{"id":"W4302763319","doi":"10.1177/2327857922111014","title":"Adverse Events in Maternal Care: Investigating Racial/Ethnic Disparities at the System Level","year":2022,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care","topic":"Maternal and fetal healthcare","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Medicine; Ethnic group; Demography; Health equity; Health care; Pregnancy; Odds ratio; Harm; Women of color; Odds; Adverse effect; Public health; Logistic regression; Race (biology); Psychology; Internal medicine; Nursing","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003257089,0.0001682682,0.0002869385,0.0001161267,0.000420917,0.00001008539,0.0003539354,0.00004873686,0.00001700869],"category_scores_gemma":[0.00002392903,0.0001140879,0.00008652975,0.00009717909,0.00007200149,0.00006940283,0.0004680407,0.0004348446,7.372963e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002526872,"about_ca_system_score_gemma":0.00009530188,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007638347,"about_ca_topic_score_gemma":0.001974297,"domain_scores_codex":[0.9984253,0.00002799764,0.0006114061,0.0002690255,0.0004195053,0.000246774],"domain_scores_gemma":[0.999265,0.00004629089,0.0003943576,0.00009482421,0.0001067323,0.00009277026],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003488343,0.00002517817,0.9816607,0.001924967,0.00001500242,9.644897e-7,0.01150303,0.0001553627,0.0004895567,0.003740961,0.00003130945,0.000104197],"study_design_scores_gemma":[0.0008274004,0.0002231719,0.9765425,0.001054295,0.000008058296,0.00002347658,0.01935729,0.0001470874,0.001206024,0.00008837017,0.0004056096,0.0001166904],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957773,0.0004044173,4.804851e-8,0.001857665,0.0006847156,0.0005534319,0.0001902436,0.00001406733,0.0005180982],"genre_scores_gemma":[0.9987562,0.00006476944,0.0000127964,0.0006171101,0.00009744069,0.0000773222,0.00003293526,0.00002064265,0.0003207276],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007854258,"threshold_uncertainty_score":0.9989699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05264790051327941,"score_gpt":0.317580769167949,"score_spread":0.2649328686546696,"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."}}