{"id":"W4410274298","doi":"10.1164/ajrccm.2025.211.abstracts.a5690","title":"Development and Nested Cross-Validation of Explainable Machine-Learning Models for Predicting the Risk of Propofol-Associated Hypertriglyceridemia in Critically-Ill, Mechanically-Ventilated Patients","year":2025,"lang":"en","type":"article","venue":"American Journal of Respiratory and Critical Care Medicine","topic":"Sepsis Diagnosis and Treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Medicine; Critically ill; Hypertriglyceridemia; Propofol; Intensive care medicine; Intensive care; Anesthesia; Internal medicine","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":[0.02189186,0.00148245,0.001278309,0.001196502,0.0005341875,0.001034596,0.001425077,0.001026137,0.0008360131],"category_scores_gemma":[0.02236743,0.0006248858,0.001920123,0.0004230634,0.0006504702,0.0006436463,0.001292367,0.002017101,0.0002794767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001040726,"about_ca_system_score_gemma":0.002216451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0125572,"about_ca_topic_score_gemma":0.006756298,"domain_scores_codex":[0.9953817,0.003389298,0.0002579802,0.0005332275,0.0002095359,0.0002282291],"domain_scores_gemma":[0.9805408,0.01533457,0.001040398,0.0007453818,0.001924378,0.0004144332],"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":[0.0006178545,0.0005230592,0.05339394,0.00006529464,0.0005119744,0.0001072734,0.0001226935,0.9164919,0.000973156,0.0005680945,0.000826049,0.02579866],"study_design_scores_gemma":[0.00001235384,0.00006788215,0.001723057,0.000007555253,0.00001504965,0.000006215706,0.000008159319,0.9978079,0.0001800806,0.000132489,0.00003435906,0.000004836939],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8062739,0.0007178506,0.1905174,0.0004791488,0.00007897971,0.0002335535,0.0005096055,0.0005768845,0.0006125953],"genre_scores_gemma":[0.9693705,0.00007461538,0.02926678,0.0000861764,0.00001769487,0.0001554374,0.0007811862,0.00003063722,0.0002169594],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02189186,"threshold_uncertainty_score":0.1157766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03957687908390115,"score_gpt":0.3421404787009631,"score_spread":0.3025635996170619,"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."}}