{"id":"W4415613349","doi":"10.1007/s43678-025-01035-9","title":"Simulation, artificial intelligence, and deep learning enhance emergency department leadership in life-threatening scenarios","year":2025,"lang":"en","type":"editorial","venue":"Canadian Journal of Emergency Medicine","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Emergency department; Deep learning; MEDLINE; Emergency response","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":[0.005095202,0.001913094,0.002322558,0.001932552,0.001453542,0.004822258,0.002588154,0.01076329,0.01166287],"category_scores_gemma":[0.02526159,0.0006497388,0.002460268,0.0008797442,0.001399564,0.002004926,0.001010403,0.01201211,0.003880408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002006915,"about_ca_system_score_gemma":0.003268731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00322512,"about_ca_topic_score_gemma":0.01351723,"domain_scores_codex":[0.9973683,0.0006299486,0.0003334273,0.0002046849,0.001319516,0.0001441339],"domain_scores_gemma":[0.976822,0.01509066,0.000507111,0.0002345326,0.005359918,0.001985795],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006963828,0.00002379708,0.00004288245,0.0005270829,0.0000647168,0.00007410695,0.00001393272,0.0001196266,0.00003287087,0.0003453513,0.9845903,0.01409564],"study_design_scores_gemma":[0.0004842938,0.0001397401,0.0009156819,0.002321721,0.0003801527,0.000357303,0.0001096299,0.001626564,0.000208661,0.004405731,0.9890064,0.00004406437],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.0001143694,0.01090056,0.0003889365,0.03564472,0.9507191,0.00003973949,0.0001065316,0.00007799634,0.002008052],"genre_scores_gemma":[0.001329794,0.01163128,0.0003441358,0.01075223,0.9698462,0.00003827536,0.00005625932,0.00003909327,0.005962738],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.01166287,"threshold_uncertainty_score":0.03901619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2813195469616137,"score_gpt":0.4383238492333416,"score_spread":0.1570043022717279,"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."}}