{"id":"W4405168559","doi":"10.3390/diagnostics14232763","title":"Machine and Deep Learning Models for Hypoxemia Severity Triage in CBRNE Emergencies","year":2024,"lang":"en","type":"article","venue":"Diagnostics","topic":"Cardiac Arrest and Resuscitation","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université de Montréal; Université du Québec à Montréal; Mila - Quebec Artificial Intelligence Institute; Centre Hospitalier Universitaire Sainte-Justine","funders":"","keywords":"Triage; Hypoxemia; Medical emergency; Disaster response; Medicine; Emergency management; Anesthesia; Political 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":[0.001061102,0.0009893954,0.000466371,0.0005636491,0.0002326944,0.000624751,0.0008227195,0.00070064,0.00146463],"category_scores_gemma":[0.002570352,0.0002746295,0.0006890101,0.000372915,0.0001787603,0.0005384607,0.0005254561,0.001262155,0.0004422362],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007292948,"about_ca_system_score_gemma":0.0009978076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01060436,"about_ca_topic_score_gemma":0.01101551,"domain_scores_codex":[0.999757,0.00006043135,0.00001532595,0.00008097404,0.00003789701,0.00004831226],"domain_scores_gemma":[0.9993362,0.0003400597,0.0000806414,0.0000259362,0.0001742499,0.00004289131],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002038291,0.0001931503,0.01076106,0.00006509719,0.00009904596,0.00007262726,0.00004334341,0.8933172,0.001694519,0.0006283748,0.002275723,0.09064612],"study_design_scores_gemma":[0.000002934039,0.00002818264,0.0006719134,0.000007724415,0.000007075934,0.000006416327,0.000005132185,0.9985071,0.0002795736,0.0003560469,0.0001250945,0.000002692076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5228779,0.002514143,0.4654236,0.002087009,0.0003321528,0.0001532623,0.001349594,0.001807429,0.003454848],"genre_scores_gemma":[0.9651365,0.0003199775,0.03114975,0.0001798585,0.00008201354,0.0001070581,0.0007797719,0.00003292249,0.00221217],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01060436,"threshold_uncertainty_score":0.02108526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02203127774617943,"score_gpt":0.2869306550651185,"score_spread":0.2648993773189391,"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."}}