{"id":"W4404346678","doi":"10.48550/arxiv.2410.23503","title":"Development and Comparative Analysis of Machine Learning Models for Hypoxemia Severity Triage in CBRNE Emergency Scenarios Using Physiological and Demographic Data from Medical-Grade Devices","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Cardiac Arrest and Resuscitation","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Université de Montréal","keywords":"Triage; Hypoxemia; Medical emergency; Medicine; Computer science; Emergency medicine; Intensive care medicine; Cardiology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003372295,0.001267242,0.0007290724,0.0009147885,0.0003309068,0.0007631214,0.00100721,0.0008798853,0.000972556],"category_scores_gemma":[0.006329204,0.0004220816,0.001002717,0.0004633139,0.0001976475,0.0008109003,0.0006715907,0.001423732,0.0006220532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001063722,"about_ca_system_score_gemma":0.001512033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01434512,"about_ca_topic_score_gemma":0.009070463,"domain_scores_codex":[0.9992191,0.000279446,0.00007275464,0.0001976403,0.0001409501,0.00009018258],"domain_scores_gemma":[0.9973516,0.0016703,0.0001158378,0.0001278799,0.0006282132,0.0001060937],"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":[0.0004081775,0.0003546165,0.01689454,0.0001461058,0.0002418997,0.0001160428,0.00009174946,0.8244364,0.002396456,0.0009143295,0.002047226,0.1519524],"study_design_scores_gemma":[0.00000517633,0.00008596024,0.001243043,0.000009576393,0.0000164895,0.00001024102,0.00001452729,0.9972516,0.0009216276,0.0002187945,0.0002165488,0.000006372819],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6738636,0.002745383,0.3124713,0.001400246,0.0003365342,0.0003878464,0.001105892,0.003118803,0.004570359],"genre_scores_gemma":[0.9334996,0.0005346542,0.06263596,0.0001464165,0.0000491312,0.0002249859,0.001299869,0.00006176189,0.001547525],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01434512,"threshold_uncertainty_score":0.02852321,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2638112258033544,"score_gpt":0.2957153674846349,"score_spread":0.03190414168128047,"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."}}