{"id":"W4404515213","doi":"10.1007/s43678-024-00807-z","title":"Machine learning outperforms the Canadian Triage and Acuity Scale (CTAS) in predicting need for early critical care","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Emergency Medicine","topic":"Emergency and Acute Care Studies","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Royal Victoria Hospital; McGill University; Jewish General Hospital","funders":"Social Sciences and Humanities Research Council","keywords":"Triage; Emergency department; Medicine; Scale (ratio); Medical emergency; Machine learning; Emergency medicine; Nursing; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00463928,0.0008382014,0.001071342,0.001057471,0.0006174672,0.001397991,0.0009067316,0.001331687,0.003205892],"category_scores_gemma":[0.02301924,0.0001954903,0.0008879302,0.0006034439,0.0004054971,0.001521387,0.0007926289,0.001493988,0.001189289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001484112,"about_ca_system_score_gemma":0.003362383,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08443448,"about_ca_topic_score_gemma":0.07792017,"domain_scores_codex":[0.9983941,0.0005406961,0.0001364286,0.0003211644,0.000406488,0.0002012043],"domain_scores_gemma":[0.9874396,0.008417687,0.0006651148,0.0006330512,0.00195322,0.0008914762],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004657757,0.001366013,0.6026137,0.0002567577,0.00106834,0.0001809665,0.000155528,0.04691689,0.0007585522,0.0009495603,0.0410878,0.2999881],"study_design_scores_gemma":[0.0004426502,0.00207788,0.3374498,0.0002480049,0.0004989752,0.0002693534,0.0004397042,0.6442971,0.0009670849,0.005098017,0.008090117,0.0001212723],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.957521,0.005910211,0.008998423,0.007800977,0.001525116,0.0001797299,0.00513877,0.0008628255,0.01206298],"genre_scores_gemma":[0.9871228,0.0008904074,0.004300423,0.000876231,0.0003085854,0.00003361303,0.003793628,0.00004910623,0.002625143],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9155655,"threshold_uncertainty_score":0.167886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0633651488236762,"score_gpt":0.356954447403775,"score_spread":0.2935892985800987,"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."}}