{"id":"W3043592225","doi":"10.1136/bmjoq-2020-000969","title":"Optimising after-hours workflow of computed tomography orders in the emergency department","year":2020,"lang":"en","type":"article","venue":"BMJ Open Quality","topic":"Radiology practices and education","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; University of Toronto","funders":"","keywords":"Medicine; Emergency department; Workflow; Turnaround time; Radiology; Concordance; Emergency medicine; Medical emergency; Nursing; Operations management; Internal medicine; Computer science","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":[0.00643763,0.0006004851,0.0004376362,0.001137232,0.0007854867,0.001618429,0.001246136,0.0005293402,0.001616245],"category_scores_gemma":[0.02091888,0.0004215564,0.0004886863,0.0008047585,0.0004074372,0.001196264,0.001082446,0.0005303933,0.0005521381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001452124,"about_ca_system_score_gemma":0.005521292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005054597,"about_ca_topic_score_gemma":0.007857755,"domain_scores_codex":[0.9945891,0.003189442,0.0003502669,0.0003792508,0.0008012176,0.0006907635],"domain_scores_gemma":[0.9894494,0.003965834,0.002789109,0.0007026434,0.001355448,0.001737439],"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":[0.003149795,0.008100748,0.136541,0.001848377,0.0001783201,0.0004168478,0.01409349,0.01381967,0.0154408,0.0009392373,0.00516338,0.8003083],"study_design_scores_gemma":[0.001147977,0.01870786,0.8532896,0.00156294,0.0004256965,0.0006796069,0.01884399,0.05958501,0.02218038,0.003354258,0.01987367,0.00034913],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9819718,0.0004116268,0.01298706,0.0007181309,0.00005868588,0.0007423983,0.0001229413,0.0004735261,0.00251389],"genre_scores_gemma":[0.9609425,0.0003423381,0.03717038,0.0001480004,0.00004301872,0.0004149814,0.0001622671,0.0000423711,0.0007340871],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00643763,"threshold_uncertainty_score":0.03404588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1613096324986311,"score_gpt":0.4624731898680385,"score_spread":0.3011635573694075,"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."}}