{"id":"W4398794240","doi":"10.1101/2024.05.22.24306395","title":"Reducing PIA by over 50% While Generating New Patient Flows: A Comprehensive Assessment of Emergency Department Redesign","year":2024,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Emergency and Acute Care Studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Southlake Regional Health Center; University of Toronto","funders":"","keywords":"Emergency department; Medical emergency; Operations management; Medicine; Computer science; Engineering; Nursing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0230054,0.001656057,0.0009979926,0.003728354,0.001229165,0.003358967,0.001760745,0.001514754,0.001972477],"category_scores_gemma":[0.03690831,0.0003965451,0.002834032,0.002082521,0.00111386,0.003087231,0.003071276,0.001566122,0.0002379085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006815364,"about_ca_system_score_gemma":0.01135472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006090971,"about_ca_topic_score_gemma":0.01176374,"domain_scores_codex":[0.9822668,0.008793049,0.00124848,0.000600455,0.005443944,0.001647127],"domain_scores_gemma":[0.9678519,0.01619926,0.005370169,0.001220446,0.006368859,0.002989289],"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":[0.002818566,0.01141779,0.1082463,0.008457816,0.00236509,0.0002382438,0.003087255,0.05288887,0.002908889,0.00517431,0.00960116,0.7927957],"study_design_scores_gemma":[0.002485655,0.1314747,0.6907761,0.01081877,0.005962251,0.0005420036,0.01558151,0.05367893,0.00892777,0.01169129,0.06749478,0.0005663522],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9392479,0.01586624,0.007865392,0.01262319,0.0002310612,0.005716794,0.0008894574,0.0004910238,0.01706892],"genre_scores_gemma":[0.9699459,0.007234963,0.01736786,0.002456398,0.000167747,0.001326301,0.0005399725,0.00003439095,0.0009264802],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0230054,"threshold_uncertainty_score":0.1216656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0419978876728613,"score_gpt":0.3333235811229717,"score_spread":0.2913256934501104,"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."}}