{"id":"W4310272037","doi":"10.1186/s12944-022-01732-9","title":"Evaluating the use of novel atherogenicity indices and insulin resistance surrogate markers in predicting the risk of coronary artery disease: a case‒control investigation with comparison to traditional biomarkers","year":2022,"lang":"en","type":"article","venue":"Lipids in Health and Disease","topic":"Diabetes, Cardiovascular Risks, and Lipoproteins","field":"Medicine","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sinai Health System; Lunenfeld-Tanenbaum Research Institute","funders":"","keywords":"Insulin resistance; Medicine; Internal medicine; Body mass index; Confounding; Coronary artery disease; Lipidology; Clinical nutrition; Triglyceride; Diabetes mellitus; Cholesterol; Endocrinology; Insulin","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":[0.002534759,0.0001293274,0.000345836,0.0001294604,0.0002922794,0.00001436656,0.00006355048,0.00002007969,0.000005288633],"category_scores_gemma":[0.001194854,0.00008866633,0.00007001375,0.0003723873,0.0002497206,0.00009131591,0.00004191083,0.0002646485,3.685515e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007529013,"about_ca_system_score_gemma":0.0006590114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003624132,"about_ca_topic_score_gemma":0.001480414,"domain_scores_codex":[0.9974026,0.001083171,0.0005219071,0.0002798571,0.0004950836,0.0002173964],"domain_scores_gemma":[0.997987,0.0009017673,0.0003110213,0.0002718682,0.00004247676,0.0004858201],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.009176822,0.0002552268,0.9576183,0.0007886815,0.0001763716,0.00004501909,0.002581825,0.007455421,0.000006084378,0.00002423965,0.00004800585,0.02182398],"study_design_scores_gemma":[0.004066359,0.000510881,0.9496986,0.0004322461,0.0002068692,0.00002294196,0.001824301,0.0430191,0.000001118973,0.00007401377,0.00006317953,0.0000804391],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9874279,0.008285508,0.00007314629,0.001474877,0.00004597909,0.001733621,0.0009468367,0.000009869163,0.000002239986],"genre_scores_gemma":[0.9980556,0.0001211248,0.0005856201,0.0008281123,0.00004641505,0.0002988403,0.00004856384,0.00001283071,0.000002847008],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03556368,"threshold_uncertainty_score":0.5478628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07731903970535826,"score_gpt":0.3034276155292719,"score_spread":0.2261085758239136,"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."}}