{"id":"W2783077854","doi":"10.3233/978-1-61499-830-3-1108","title":"A Comparison of Two Principal Systems for Monitoring of Technology-Induced Errors in Electronic Health Records","year":2017,"lang":"en","type":"article","venue":"Studies in health technology and informatics","topic":"Quality and Safety in Healthcare","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Harm; Health information technology; Principal (computer security); Health records; Computer science; Protocol (science); Legislation; Medical emergency; Risk analysis (engineering); Computer security; Data science; Medicine; Health care; Psychology; Political science; Alternative medicine","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":[0.08949587,0.0008861287,0.001970303,0.02185446,0.001329745,0.006951042,0.002044093,0.002259511,0.003548742],"category_scores_gemma":[0.2759626,0.0009792132,0.004938486,0.01607449,0.001499149,0.007108709,0.004718967,0.001022111,0.00074948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006870982,"about_ca_system_score_gemma":0.008437888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004661232,"about_ca_topic_score_gemma":0.006673431,"domain_scores_codex":[0.8066866,0.1173347,0.03435349,0.005120469,0.0350978,0.001406911],"domain_scores_gemma":[0.5681808,0.3139679,0.04079232,0.01209368,0.06346898,0.001496325],"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":[0.02553444,0.00180531,0.1417654,0.1194383,0.006874469,0.0002824524,0.01471726,0.001140413,0.003153511,0.0115801,0.004985665,0.6687225],"study_design_scores_gemma":[0.01096182,0.05131254,0.6557107,0.09971052,0.0424038,0.002282283,0.0278455,0.007615896,0.01566336,0.007402897,0.07820538,0.0008853246],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5831647,0.262658,0.05705882,0.00611334,0.001939865,0.02834114,0.008389297,0.0005643825,0.05177044],"genre_scores_gemma":[0.793196,0.07436182,0.102949,0.001381493,0.0005171564,0.01950163,0.004854157,0.0001185222,0.003120234],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08949587,"threshold_uncertainty_score":0.4733051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2710868764981271,"score_gpt":0.5703073937158222,"score_spread":0.299220517217695,"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."}}