{"id":"W4378782572","doi":"10.2196/34453","title":"Patient Safety of Perioperative Medication Through the Lens of Digital Health and Artificial Intelligence","year":2023,"lang":"en","type":"article","venue":"JMIR Perioperative Medicine","topic":"Patient Safety and Medication Errors","field":"Health Professions","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Patient safety; Perioperative; Medicine; Psychological intervention; Health care; Medical emergency; Intensive care medicine; Nursing; Surgery","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007515211,0.0001937042,0.0005316213,0.0001198896,0.0007298785,0.000001125222,0.0001645972,0.0001032483,0.0003894553],"category_scores_gemma":[0.001283929,0.0001119394,0.00004444741,0.0007048891,0.001400851,0.0002499503,0.0001200864,0.0004422195,0.00005119426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009107422,"about_ca_system_score_gemma":0.0006010585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001282863,"about_ca_topic_score_gemma":0.00007505379,"domain_scores_codex":[0.9969304,0.0005334329,0.001313287,0.0003076001,0.0005750961,0.000340171],"domain_scores_gemma":[0.9976537,0.0007627707,0.0006964384,0.0003374121,0.0004015453,0.0001481167],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0004053998,0.0001475868,0.004794714,0.0004267077,0.00007484297,0.000001451485,0.8786955,0.0001213957,0.001264985,0.08995648,0.001325837,0.02278507],"study_design_scores_gemma":[0.001344668,0.003690625,0.0419817,0.002337839,0.0000386635,0.000004826844,0.7790921,0.002007083,0.0006542058,0.001141031,0.1673664,0.0003408908],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6355031,0.001938071,0.0255208,0.2973049,0.0021033,0.008498476,0.0005179154,0.0002338373,0.02837965],"genre_scores_gemma":[0.9958521,0.001008971,0.00008803889,0.001728728,0.0001762776,0.0003026066,0.0002395204,0.00001714425,0.0005866435],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.360349,"threshold_uncertainty_score":0.561371,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1438911821855422,"score_gpt":0.4572732484352827,"score_spread":0.3133820662497405,"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."}}