{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002187546,0.00008499451,0.0001700841,0.00008965123,0.00004275472,0.00002173053,0.00001871268,0.00006740822,0.0000107562],"category_scores_gemma":[0.001021562,0.0000728936,0.00006082295,0.0001556777,0.00002877618,0.00006661552,0.00002183938,0.0001694316,0.000005336959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003128695,"about_ca_system_score_gemma":0.00002852465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000384553,"about_ca_topic_score_gemma":0.00005071036,"domain_scores_codex":[0.999422,0.00002112196,0.0001528804,0.0001615989,0.0001021056,0.0001403435],"domain_scores_gemma":[0.9990972,0.0007231581,0.00001589784,0.00006789529,0.00003674391,0.00005912737],"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":[0.0009150972,0.0004210351,0.452059,0.004354509,0.0004683114,0.0008359061,0.01822522,0.01638117,0.00220738,0.02533793,0.01316095,0.4656335],"study_design_scores_gemma":[0.002327245,0.0002062449,0.1880123,0.0005081269,0.0003340404,0.00001553818,0.0008330072,0.7593065,0.0003166657,0.00907109,0.03875069,0.0003185555],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9454469,0.03236033,0.01641667,0.001170539,0.001757782,0.0007703078,0.00004244216,0.000132478,0.001902514],"genre_scores_gemma":[0.9903132,0.008134225,0.0008935611,0.00006505343,0.0002483721,0.00004231632,0.00009281936,0.00001861647,0.0001918052],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7429253,"threshold_uncertainty_score":0.2972515,"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."}}