{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001716153,0.0008773144,0.001822516,0.0004864428,0.0003255524,0.0001989355,0.0002471472,0.000378896,0.00009169275],"category_scores_gemma":[0.0006488131,0.0007805653,0.002632501,0.0004038919,0.0002070326,0.0004406627,0.0002144616,0.004130116,0.0002219368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005378051,"about_ca_system_score_gemma":0.0003665246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000697997,"about_ca_topic_score_gemma":0.000007080864,"domain_scores_codex":[0.9942682,0.0005029817,0.0006567129,0.001177283,0.00211075,0.001284058],"domain_scores_gemma":[0.9958791,0.0004065659,0.0002818283,0.002582411,0.0005428256,0.000307294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009786824,0.00005095649,0.1566084,0.0009179258,0.006514918,0.01351278,0.0002168012,0.0005106752,0.00002696006,4.138921e-7,0.8094442,0.01209814],"study_design_scores_gemma":[0.003086571,0.000078404,0.05653908,0.001713891,0.01591263,0.0149251,0.0001454988,0.0008059562,0.0002854673,0.00001283309,0.9055357,0.0009588938],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"review","genre_gemma":"commentary","genre_scores_codex":[0.02529004,0.4030933,0.1375499,0.3385829,0.02295471,0.01072295,0.003745329,0.006049384,0.05201148],"genre_scores_gemma":[0.2535304,0.007540785,0.05801788,0.4916952,0.1588266,0.001377845,0.01955335,0.003291361,0.006166601],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.3955525,"threshold_uncertainty_score":0.9994645,"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."}}