{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004055969,0.0006036631,0.0006764644,0.000962087,0.0002202245,0.001262769,0.0004220337,0.0008619681,0.0008201653],"category_scores_gemma":[0.00624731,0.0002822345,0.0003132307,0.0005873143,0.0003448987,0.0006104634,0.0004243782,0.000675696,0.0005009465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002067753,"about_ca_system_score_gemma":0.0003609027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000657399,"about_ca_topic_score_gemma":0.0007919638,"domain_scores_codex":[0.9977959,0.001247345,0.0001315689,0.0003799906,0.000345184,0.0001000134],"domain_scores_gemma":[0.995284,0.002803771,0.0008526085,0.0003292394,0.0004125951,0.0003178633],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002907537,0.0001231407,0.9673726,0.00007372918,0.0003166147,0.00007812324,0.00006922643,0.0009451904,0.004998775,0.0001707733,0.0002453115,0.02269912],"study_design_scores_gemma":[0.0001061242,0.002534536,0.9776954,0.00004543896,0.0002950965,0.0005805636,0.00009837578,0.01428377,0.002634047,0.0006933621,0.0009903117,0.0000428746],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9848273,0.006707191,0.003954568,0.0003146204,0.0000592259,0.0000787411,0.0007082858,0.00006775425,0.003282399],"genre_scores_gemma":[0.9964653,0.0005403381,0.002320839,0.00006221422,0.00005376802,0.00002960804,0.0002671592,0.000003703585,0.0002569598],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004055969,"threshold_uncertainty_score":0.02145022,"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."}}