{"id":"W2888195583","doi":"10.1186/s12933-018-0759-z","title":"Development of predictive risk models for major adverse cardiovascular events among patients with type 2 diabetes mellitus using health insurance claims data","year":2018,"lang":"en","type":"article","venue":"Cardiovascular Diabetology","topic":"Diabetes Treatment and Management","field":"Medicine","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"Group for Research in Decision Analysis","funders":"Janssen Scientific Affairs","keywords":"Medicine; Mace; Myocardial infarction; Diabetes mellitus; Internal medicine; Disease; Stroke (engine); Type 2 Diabetes Mellitus; Heart failure; Emergency medicine; Angina; Medical record; Population; Intensive care medicine; Environmental health; Conventional PCI","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00126726,0.000428741,0.001826233,0.0002408286,0.0002603496,0.000008071617,0.000390171,0.0001744851,0.000007875553],"category_scores_gemma":[0.00008444418,0.0003581725,0.0006814586,0.0004157248,0.0003419191,0.0003272468,0.0003309881,0.0001562368,0.00000856822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002739885,"about_ca_system_score_gemma":0.0003379469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005895114,"about_ca_topic_score_gemma":0.00002251432,"domain_scores_codex":[0.9964882,0.000249513,0.000640771,0.00104029,0.0007560677,0.0008251995],"domain_scores_gemma":[0.9964252,0.00005747592,0.0003213307,0.002270547,0.0006753413,0.0002501139],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004762905,0.0006636228,0.8218656,0.0008723815,0.06578444,0.000007379908,0.0007075816,0.003457794,0.0000108168,0.00002570244,0.00016814,0.1059602],"study_design_scores_gemma":[0.01647313,0.001842953,0.9454005,0.000689005,0.009723491,0.000002280545,0.0001916036,0.00953542,0.001088314,0.0001881903,0.01410361,0.0007615476],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9619563,0.01086411,0.02323135,0.000008289873,0.0004896488,0.002978277,0.0002920846,0.00007471093,0.0001051967],"genre_scores_gemma":[0.9639148,0.0004025582,0.03405917,0.00005487077,0.0001497532,0.0001603147,0.00115567,0.00008709415,0.00001581382],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1235348,"threshold_uncertainty_score":0.999887,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0272730602433564,"score_gpt":0.2519858197481212,"score_spread":0.2247127595047648,"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."}}