{"id":"W2619446037","doi":"10.1001/jamacardio.2017.1460","title":"Accuracy of Medical Claims for Identifying Cardiovascular and Bleeding Events After Myocardial Infarction","year":2017,"lang":"en","type":"article","venue":"JAMA Cardiology","topic":"Acute Myocardial Infarction Research","field":"Medicine","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Duke Clinical Research Institute; National Institutes of Health; Genentech; Valeant Pharmaceuticals International; Ministério da Ciência, Tecnologia e Inovação; HeartWare; Regeneron Pharmaceuticals; Boston Scientific Corporation; Gilead Sciences; GlaxoSmithKline; National Heart, Lung, and Blood Institute; Thoratec Corporation; Pfizer; Eli Lilly and Company; Bristol-Myers Squibb; AstraZeneca; Daiichi Sankyo Europe; Sanofi; American Heart Association","keywords":"Medicine; Myocardial infarction; Cardiology; Internal medicine; MEDLINE; Intensive care medicine","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":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.1917344,0.0006685724,0.001511093,0.003218293,0.0007762264,0.004650395,0.001806533,0.002594327,0.001046597],"category_scores_gemma":[0.4237935,0.0005446401,0.0018487,0.003499665,0.00232552,0.002439615,0.001924142,0.001736981,0.0004187594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001258849,"about_ca_system_score_gemma":0.001384929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002007572,"about_ca_topic_score_gemma":0.001911895,"domain_scores_codex":[0.7530689,0.1815705,0.02460634,0.009030924,0.0299156,0.001807748],"domain_scores_gemma":[0.431971,0.400773,0.09493442,0.04329577,0.02696502,0.002060752],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005249759,0.0001649977,0.9497592,0.0004927898,0.002895109,0.00004743275,0.0004063809,0.002423199,0.0002372045,0.001426701,0.001783689,0.03511364],"study_design_scores_gemma":[0.0007535574,0.001576928,0.9493172,0.0008320528,0.00280792,0.0003842025,0.0002536219,0.0324423,0.001789317,0.005384914,0.004339932,0.000118112],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9073946,0.02284694,0.03621271,0.008149577,0.0009759429,0.001077958,0.007199148,0.0002408745,0.01590234],"genre_scores_gemma":[0.9935802,0.0005426395,0.004103517,0.0004403558,0.0002310936,0.00008773365,0.0008277923,0.00001385786,0.0001727546],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8082656,"threshold_uncertainty_score":0.9967353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05652740741845014,"score_gpt":0.3773259866103664,"score_spread":0.3207985791919163,"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."}}