{"id":"W2278347241","doi":"10.1016/s1701-2163(15)30178-x","title":"Learning from Adverse Events in Obstetrics: Is a Standardized Computer Tool an Effective Strategy for Root Cause Analysis?","year":2015,"lang":"en","type":"article","venue":"Journal of Obstetrics and Gynaecology Canada","topic":"Patient Safety and Medication Errors","field":"Health Professions","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hamilton Health Sciences; McMaster Children's Hospital; McMaster University","funders":"","keywords":"Medicine; Root cause analysis; Root (linguistics); Obstetrics; Adverse effect; Medical physics; Intensive care medicine; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.02456959,0.0007442885,0.001220722,0.003592339,0.002094243,0.003845867,0.002274248,0.002898368,0.005881375],"category_scores_gemma":[0.188478,0.000718536,0.001229348,0.002284025,0.001585403,0.006227601,0.00329999,0.004064258,0.001164128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003815797,"about_ca_system_score_gemma":0.03037552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02083693,"about_ca_topic_score_gemma":0.04682034,"domain_scores_codex":[0.9785917,0.0104351,0.003346161,0.0008093147,0.005713275,0.001104291],"domain_scores_gemma":[0.8691601,0.06676383,0.01760426,0.005711915,0.02852299,0.01223691],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002871582,0.002662538,0.1258612,0.001336909,0.0001810369,0.0004442731,0.003498166,0.0007548621,0.0003209756,0.001482237,0.04462445,0.8185462],"study_design_scores_gemma":[0.001730967,0.007018965,0.8199204,0.02176577,0.001110044,0.003808342,0.04260457,0.01202641,0.002934894,0.02422926,0.06202171,0.0008287574],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.3922207,0.01950084,0.03603005,0.490953,0.00406793,0.002951762,0.0008870416,0.001742264,0.05164635],"genre_scores_gemma":[0.8490174,0.0226824,0.08851299,0.03264356,0.00248619,0.00128333,0.0007080311,0.0002294228,0.002436668],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02456959,"threshold_uncertainty_score":0.1299379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05370910766412587,"score_gpt":0.3571536318123258,"score_spread":0.3034445241482,"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."}}