{"id":"W4406723684","doi":"10.1136/bmjopen-2024-092594","title":"Development and validation of machine-learning models for predicting the risk of hypertriglyceridemia in critically ill patients receiving propofol sedation using retrospective data: a protocol","year":2025,"lang":"en","type":"article","venue":"BMJ Open","topic":"Hyperglycemia and glycemic control in critically ill and hospitalized patients","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Heart, Lung, and Blood Institute; National Institutes of Health","keywords":"Medicine; Propofol; Machine learning; Hypertriglyceridemia; Sedation; Feature selection; Intensive care medicine; Artificial intelligence; Emergency medicine; Anesthesia; Computer science; 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":[0.07030134,0.001673168,0.001210502,0.001099637,0.001283572,0.001697716,0.00328007,0.002093661,0.006354992],"category_scores_gemma":[0.08712254,0.001233609,0.002791666,0.001035682,0.001639535,0.0009527599,0.002310642,0.002159706,0.002166599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001934898,"about_ca_system_score_gemma":0.01161701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001611064,"about_ca_topic_score_gemma":0.001495322,"domain_scores_codex":[0.9740747,0.01903589,0.002260128,0.001771021,0.002392084,0.0004662211],"domain_scores_gemma":[0.9387523,0.02637457,0.005105176,0.01219529,0.01620303,0.00136966],"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":[0.05725908,0.02514893,0.1348064,0.009239393,0.002855271,0.00184911,0.004198928,0.2312027,0.01824851,0.01801311,0.02636175,0.4708169],"study_design_scores_gemma":[0.03024615,0.0995645,0.2088947,0.009744665,0.003537175,0.001880922,0.002647436,0.4414274,0.06122494,0.01851464,0.1212547,0.001062836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"protocol","genre_gemma":"protocol","genre_scores_codex":[0.1851721,0.0006390957,0.2275442,0.0008864109,0.0002199253,0.5694781,0.01087098,0.0005556093,0.004633602],"genre_scores_gemma":[0.1274285,0.0005357442,0.1711925,0.0004057478,0.00005665395,0.689952,0.008701827,0.00007114738,0.001655923],"genre_candidate":"protocol","genre_consensus":"protocol","teacher_disagreement_score":0.07030134,"threshold_uncertainty_score":0.3717934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08115706411503913,"score_gpt":0.3953172381365274,"score_spread":0.3141601740214883,"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."}}