{"id":"W3082744202","doi":"10.1017/ice.2020.346","title":"Nudging empiric prescribing: Embedding antimicrobial stewardship program order sets into a general medicine admission order set","year":2020,"lang":"en","type":"article","venue":"Infection Control and Hospital Epidemiology","topic":"Antibiotic Use and Resistance","field":"Immunology and Microbiology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Public Health Agency of Canada; St Joseph's Health Centre","funders":"","keywords":"Antimicrobial stewardship; Order (exchange); Set (abstract data type); Stewardship (theology); Embedding; Medicine; Antimicrobial; Computer science; Business; Artificial intelligence; Microbiology; Political science; Programming language; Biology; Antibiotics; Law","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000679728,0.0003631126,0.0009414033,0.000112799,0.0004349842,0.00001560237,0.00013242,0.0005464155,0.0002205114],"category_scores_gemma":[0.002516355,0.0002711366,0.000119534,0.0002472723,0.0005126832,0.0001518193,0.0001008282,0.0005813427,0.00008686195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003501447,"about_ca_system_score_gemma":0.00008456231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009685595,"about_ca_topic_score_gemma":0.00005425952,"domain_scores_codex":[0.9971394,0.000756503,0.0007262224,0.0007046614,0.00003991866,0.0006332886],"domain_scores_gemma":[0.9986323,0.0004985455,0.0003396437,0.0001877598,0.000193354,0.0001484238],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002045893,0.0008107926,0.5442643,0.0008247403,0.001838021,0.00007614114,0.008124466,0.001328385,0.1876472,0.002455174,0.1256301,0.1249548],"study_design_scores_gemma":[0.03534869,0.01758509,0.2737727,0.001169539,0.001410949,0.0006271829,0.00108171,0.03512312,0.005234025,0.002887764,0.6222212,0.003537985],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9388026,0.004899083,0.01538543,0.03745082,0.001755291,0.0009085682,0.00001937434,0.0004185999,0.0003602626],"genre_scores_gemma":[0.9904276,0.0005431456,0.001024046,0.007174455,0.0004217524,0.00002865034,0.0001167122,0.00003072236,0.0002329022],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4965912,"threshold_uncertainty_score":0.9999741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02995160591893018,"score_gpt":0.3276618657036749,"score_spread":0.2977102597847447,"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."}}