{"id":"W2210269229","doi":"10.1016/j.jcmg.2012.08.005","title":"Enhancing Risk Prediction With PET Coronary Flow Reserve","year":2012,"lang":"en","type":"letter","venue":"JACC. Cardiovascular imaging","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hamilton Health Sciences; McMaster University; Population Health Research Institute","funders":"","keywords":"Psychological intervention; Selection (genetic algorithm); Medicine; Diagnostic test; Test (biology); Coronary flow reserve; Fractional flow reserve; Medical physics; Intensive care medicine; Computer science; Internal medicine; Machine learning; Coronary artery disease; Coronary angiography; Emergency medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001166893,0.0006728397,0.001084446,0.0005568149,0.0009115739,0.001645801,0.0006730487,0.01359027,0.003502373],"category_scores_gemma":[0.01042699,0.0004799048,0.0007539223,0.0003498806,0.0009851548,0.001590896,0.0006220316,0.01325474,0.003252974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001114586,"about_ca_system_score_gemma":0.0006452571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001612524,"about_ca_topic_score_gemma":0.003762386,"domain_scores_codex":[0.9992012,0.0003034397,0.0001066893,0.000093763,0.000188672,0.0001061814],"domain_scores_gemma":[0.9967339,0.002337396,0.00009662526,0.000127777,0.0004586952,0.000245762],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0006361406,0.0002006466,0.02130746,0.000193269,0.0000899189,0.04381142,0.0002130892,0.0005234582,0.002502364,0.005902683,0.7741669,0.1504526],"study_design_scores_gemma":[0.000923112,0.0007075467,0.02807801,0.001102453,0.0004239388,0.156199,0.0007042615,0.01181028,0.004869917,0.07101358,0.7239305,0.0002373747],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.008078627,0.004650787,0.00109736,0.956794,0.0116106,0.00003914201,0.0001113118,0.00009694302,0.01752113],"genre_scores_gemma":[0.1319222,0.007515383,0.004712947,0.6740538,0.161608,0.0001148444,0.0001880591,0.00007956895,0.01980525],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01359027,"threshold_uncertainty_score":0.0117166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008634210688254807,"score_gpt":0.2177546949470592,"score_spread":0.2091204842588044,"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."}}