{"id":"W3109883567","doi":"10.1111/cts.12886","title":"Performance of Plasma Adenosine as a Biomarker for Predicting Cardiovascular Risk","year":2020,"lang":"en","type":"article","venue":"Clinical and Translational Science","topic":"Adenosine and Purinergic Signaling","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; University of Ottawa","funders":"Canadian Institutes of Health Research; Royal College of Physicians; Royal College of Physicians and Surgeons of Canada","keywords":"Mace; Medicine; Adenosine; Hazard ratio; Internal medicine; Cardiology; Myocardial infarction; Coronary artery disease; Revascularization; Angina; Percutaneous coronary intervention; Confidence interval","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005557411,0.00005888962,0.0001159997,0.00001259319,0.0001151187,0.000007882467,0.0001116829,0.00005105362,0.000007310717],"category_scores_gemma":[0.0002810936,0.00004888686,0.0001201369,0.0001213,0.0003775039,0.000008469723,0.00002721204,0.00004809888,8.660925e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":4.700073e-7,"about_ca_system_score_gemma":0.00009031338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004354877,"about_ca_topic_score_gemma":2.278227e-7,"domain_scores_codex":[0.9992059,0.0000198094,0.0002308508,0.0002747221,0.0001630504,0.0001056014],"domain_scores_gemma":[0.9996207,0.00006445255,0.00005319338,0.00007615471,0.00008731771,0.00009820414],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008342774,0.00007617258,0.9158454,0.0000968134,0.000234614,0.000001083899,0.0001472422,0.002593545,0.03807904,0.0002587172,0.0004010067,0.04143209],"study_design_scores_gemma":[0.004529115,0.0022608,0.6650606,0.00006085269,0.0002877704,0.00002249769,0.00005899275,0.2290075,0.06425106,0.0003587687,0.03365138,0.0004506916],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9945773,0.001307677,0.003320671,0.0003256663,0.00004665166,0.0001080737,0.00002326232,0.000004326814,0.0002863481],"genre_scores_gemma":[0.9976423,0.0002564986,0.001771313,0.00009827545,0.0001997815,0.000004603673,0.00001196181,0.00000389911,0.0000114069],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2507848,"threshold_uncertainty_score":0.1993548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0403456111035452,"score_gpt":0.3071509232998049,"score_spread":0.2668053121962597,"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."}}