{"id":"W2604913322","doi":"10.1161/circoutcomes.10.suppl_3.125","title":"Abstract 125: Natural Language Processing Identifies an Association Between Canadian Cardiovascular Society Angina Severity and Mortality Within the Department of Veterans Affairs","year":2017,"lang":"en","type":"article","venue":"Circulation Cardiovascular Quality and Outcomes","topic":"Cardiovascular Health and Risk Factors","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Veterans Affairs; Angina; Poisson regression; Canadian Cardiovascular Society; Electronic health record; Acute coronary syndrome; Cohort; Coronary artery disease; Internal medicine; Database; Gerontology; Myocardial infarction; Population; Health care; Environmental health","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002718405,0.000484383,0.0003357697,0.002516652,0.001020339,0.001560613,0.0008695899,0.0005184836,0.005825417],"category_scores_gemma":[0.02140157,0.000174928,0.0008803172,0.002185075,0.0005348144,0.0004717793,0.000649053,0.0006929563,0.0007685415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005856084,"about_ca_system_score_gemma":0.01074121,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7053677,"about_ca_topic_score_gemma":0.5616786,"domain_scores_codex":[0.9982406,0.0003125524,0.0001712881,0.0004080637,0.0007027709,0.0001647105],"domain_scores_gemma":[0.9865456,0.006710121,0.001958141,0.0004342493,0.004028811,0.0003230842],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004487911,0.0001920643,0.8994805,0.0009117736,0.0004533401,0.0006159989,0.0008311502,0.001669836,0.002648192,0.0006578893,0.04626532,0.04582516],"study_design_scores_gemma":[0.00009400101,0.00007810069,0.9768559,0.0001589756,0.0002681228,0.0004631397,0.0006054746,0.01225836,0.001095749,0.0008678645,0.007206201,0.00004821584],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8136485,0.001911667,0.01373563,0.006778997,0.0002115166,0.0009134768,0.1501666,0.001283423,0.01135017],"genre_scores_gemma":[0.9363661,0.0004486655,0.01068982,0.0009452803,0.0001223492,0.0002504798,0.04857071,0.00009328363,0.002513325],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2946323,"threshold_uncertainty_score":0.5927348,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04288422652348498,"score_gpt":0.3368838053246825,"score_spread":0.2939995788011975,"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."}}