{"id":"W4281749331","doi":"10.3233/shti220114","title":"Using Publicly Available Recall and Safety Alert Reports for Learning from Technology-Induced Error","year":2022,"lang":"en","type":"article","venue":"Studies in health technology and informatics","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Michael Smith Health Research BC; University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Michael Smith Health Research BC","keywords":"Recall; Patient safety; Computer science; Health care; Precision and recall; Information technology; Risk analysis (engineering); Knowledge management; Data science; Artificial intelligence; Medicine; Psychology; Cognitive psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.06528581,0.0006785493,0.0006374898,0.01597596,0.0009418632,0.004362581,0.002051392,0.001134571,0.002423895],"category_scores_gemma":[0.3190665,0.0003997287,0.000903826,0.01014335,0.0009433379,0.004265716,0.00244761,0.001361425,0.0009018037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003910241,"about_ca_system_score_gemma":0.009787827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04382616,"about_ca_topic_score_gemma":0.03991225,"domain_scores_codex":[0.9249634,0.0247596,0.01599444,0.003335842,0.02935407,0.001592537],"domain_scores_gemma":[0.3809545,0.3073904,0.1695727,0.03451585,0.1062251,0.001341534],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003189327,0.0002914811,0.7088425,0.002453292,0.0003151319,0.0002714942,0.01172563,0.001155545,0.001009252,0.00172846,0.01232586,0.2595625],"study_design_scores_gemma":[0.00009654303,0.001019838,0.8579391,0.006549905,0.001014885,0.001289171,0.02525947,0.007788942,0.01632962,0.003089481,0.07919107,0.0004319783],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.804669,0.01113132,0.0747655,0.01527872,0.0006941368,0.002584908,0.03976994,0.003398573,0.04770794],"genre_scores_gemma":[0.9384323,0.004194264,0.04145121,0.001001047,0.0002967357,0.0006101567,0.01046275,0.0001355943,0.003415859],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06528581,"threshold_uncertainty_score":0.3452685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2218896311132597,"score_gpt":0.4692529100743659,"score_spread":0.2473632789611062,"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."}}