{"id":"W3176396634","doi":"10.2139/ssrn.3857668","title":"Achieving Clinical Automation in Emergency Medicine with Machine Learning Medical Directives","year":2021,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Genome Canada; Fleming College; Hospital for Sick Children; SickKids Foundation; University of Toronto; University Health Network; Canada Research Chairs","funders":"","keywords":"Automation; Medical emergency; Medicine; Artificial intelligence; Computer science; Engineering; Mechanical engineering","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":[0.004694715,0.0006644999,0.0006332033,0.001052171,0.0007613585,0.00284652,0.001088173,0.001373593,0.004766657],"category_scores_gemma":[0.030758,0.0004610434,0.0004403726,0.0008016832,0.001190095,0.002782425,0.002855053,0.0024834,0.002674379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006364032,"about_ca_system_score_gemma":0.001926272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001049844,"about_ca_topic_score_gemma":0.001045509,"domain_scores_codex":[0.9939893,0.003145002,0.0004251016,0.0007866398,0.001322158,0.0003317714],"domain_scores_gemma":[0.9827061,0.01199614,0.001403322,0.0017946,0.001756116,0.0003437256],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007755558,0.0008931559,0.01048816,0.0004510094,0.00006422717,0.0006361056,0.001755965,0.06204779,0.01237826,0.04077452,0.01485836,0.8548769],"study_design_scores_gemma":[0.0002619788,0.001013894,0.007583733,0.0003815006,0.0000888806,0.002001583,0.001239225,0.7338853,0.06155252,0.1425788,0.04924038,0.0001723023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06258142,0.0004667854,0.9088895,0.003624946,0.0002438173,0.000277899,0.0002210324,0.006970354,0.01672425],"genre_scores_gemma":[0.7031246,0.00027779,0.2922622,0.0007285724,0.000120618,0.0001253827,0.0003020038,0.0002201586,0.002838662],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004766657,"threshold_uncertainty_score":0.02482831,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01824877534620318,"score_gpt":0.349619883738453,"score_spread":0.3313711083922499,"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."}}