{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01552474,0.002077021,0.001224168,0.004318543,0.001188341,0.005566149,0.002075055,0.003662162,0.003969873],"category_scores_gemma":[0.03284891,0.0007056917,0.0009649281,0.001908306,0.006046923,0.003566903,0.002364459,0.002680843,0.0006328727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004462538,"about_ca_system_score_gemma":0.005578751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004705855,"about_ca_topic_score_gemma":0.004272016,"domain_scores_codex":[0.9802759,0.01481686,0.00060213,0.0006071402,0.003360037,0.0003379722],"domain_scores_gemma":[0.9582248,0.03444024,0.002175896,0.0008236003,0.003477404,0.0008581589],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009141045,0.001763203,0.03625857,0.005560252,0.0008843259,0.002030895,0.005913748,0.1073561,0.004715615,0.2655507,0.0169687,0.5520838],"study_design_scores_gemma":[0.0005644645,0.008641796,0.02576127,0.004367406,0.0008955397,0.004097328,0.01428123,0.2178813,0.005934366,0.5394792,0.1775796,0.0005164277],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05459268,0.07719555,0.7562165,0.04627521,0.001956455,0.001972721,0.0001762169,0.0005519835,0.06106266],"genre_scores_gemma":[0.6115239,0.03738582,0.3297167,0.008058839,0.001393971,0.00196977,0.0001126727,0.0001030262,0.009735297],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01552474,"threshold_uncertainty_score":0.08210367,"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."}}