{"id":"W2610959882","doi":"","title":"Patient safety, potential adverse drug events, and medical device design: a human factors engineering approach","year":2001,"lang":"en","type":"article","venue":"Computers and Biomedical Research","topic":"Healthcare Technology and Patient Monitoring","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Interface (matter); Patient safety; Context (archaeology); Computer science; Adverse effect; Human error; Medicine; Procurement; Process (computing); Clinical engineering; Medical emergency; Drug; Risk analysis (engineering); Simulation; Medical physics; Pharmacology; Health care; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001287686,0.0001568814,0.0002835054,0.0003937739,0.0003147232,0.00001301801,0.0001446432,0.0003059659,0.00002976667],"category_scores_gemma":[0.0003218584,0.0001196026,0.00004070118,0.0004415822,0.0003697623,0.00004152543,0.0003685371,0.000979776,0.000003441175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008111681,"about_ca_system_score_gemma":0.0001375904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001464405,"about_ca_topic_score_gemma":0.000002923884,"domain_scores_codex":[0.9975035,0.0001818811,0.0003253582,0.0004046513,0.0009979734,0.000586654],"domain_scores_gemma":[0.9984251,0.0003352223,0.00002954064,0.0001892071,0.00009750246,0.0009234608],"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":[0.0008046826,0.001683991,0.1586542,0.001489228,0.0003486058,0.002672943,0.003740778,0.00002094473,0.002022742,0.0007130607,0.003792006,0.8240569],"study_design_scores_gemma":[0.01920378,0.01281605,0.636008,0.00680134,0.0002188869,0.003849856,0.00622287,0.183086,0.001498545,0.0007561389,0.1273835,0.002154998],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.973391,0.0004947318,0.0229928,0.002271205,0.000232173,0.0004736943,0.000002232402,0.00009965339,0.00004250131],"genre_scores_gemma":[0.9957617,0.0003702951,0.00344454,0.00008377375,0.0002360824,0.00001544414,0.00003650272,0.00001576493,0.00003591735],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8219019,"threshold_uncertainty_score":0.4877252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08145188557572541,"score_gpt":0.3709436575944583,"score_spread":0.2894917720187329,"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."}}