{"id":"W2743691476","doi":"10.1016/j.jacc.2017.06.030","title":"Biomarker-Based Risk Model to Predict Cardiovascular Mortality in Patients With Stable Coronary Disease","year":2017,"lang":"en","type":"article","venue":"Journal of the American College of Cardiology","topic":"IL-33, ST2, and ILC Pathways","field":"Immunology and Microbiology","cited_by":140,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian VIGOUR Centre; University of Alberta","funders":"NIH Clinical Center","keywords":"Medicine; Biomarker; Internal medicine; Proportional hazards model; Cohort; Cardiology; Natriuretic peptide; Prospective cohort study; Cohort study; Coronary artery disease; Heart failure","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":[0.0008438582,0.0002114362,0.001163905,0.0001571023,0.0002778814,0.000006137002,0.0008549496,0.00008632845,0.000003787861],"category_scores_gemma":[0.000497373,0.0001358479,0.0005662647,0.0001721778,0.0009725555,0.0001091504,0.0001879963,0.000345638,0.000003535673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001021489,"about_ca_system_score_gemma":0.0006255413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001490954,"about_ca_topic_score_gemma":0.00003201974,"domain_scores_codex":[0.9979244,0.000784296,0.0005103821,0.0002453156,0.0001659867,0.0003695746],"domain_scores_gemma":[0.9972354,0.0001005484,0.00106908,0.001166084,0.0003444077,0.00008447895],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004274133,0.0001145328,0.9609619,0.0000172523,0.001733289,0.00007216439,0.00002088001,0.03001084,0.0005256227,0.00001591956,0.001851916,0.0004015717],"study_design_scores_gemma":[0.003211031,0.0008075005,0.9902853,0.0000595731,0.000537272,0.0000297946,0.00005796383,0.00009221127,0.0002848421,0.00007881997,0.004396801,0.0001588383],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9921805,0.0003260184,0.001147539,0.0001040116,0.0004887299,0.0003629401,0.005017165,0.000006281522,0.0003668736],"genre_scores_gemma":[0.9995123,0.00006905314,0.0001898183,0.00008915642,0.00003660616,0.000009459051,0.00000963515,0.00001905129,0.00006494721],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02991863,"threshold_uncertainty_score":0.5539719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01554791852605216,"score_gpt":0.2327717964852353,"score_spread":0.2172238779591831,"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."}}