{"id":"W2760026784","doi":"10.1016/j.jcct.2017.09.009","title":"Fractional flow reserve derived from coronary computed tomography angiography reclassification rate using value distal to lesion compared to lowest value","year":2017,"lang":"en","type":"article","venue":"Journal of cardiovascular computed tomography","topic":"Coronary Interventions and Diagnostics","field":"Medicine","cited_by":72,"is_retracted":false,"has_abstract":false,"ca_institutions":"St. Paul's Hospital","funders":"","keywords":"Fractional flow reserve; Medicine; Computed tomography angiography; Radiology; Value (mathematics); Coronary angiography; Lesion; Angiography; Computed tomography; Cardiology; Nuclear medicine; Statistics; Myocardial infarction; Surgery; Mathematics","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":[0.002044958,0.0005772169,0.0007151489,0.001501286,0.000306997,0.001108036,0.0004925444,0.0007104177,0.001953632],"category_scores_gemma":[0.004736911,0.0001122918,0.0009097357,0.000434545,0.0003487578,0.0004680518,0.0004640283,0.0009157237,0.000475393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001983109,"about_ca_system_score_gemma":0.0002657984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001031204,"about_ca_topic_score_gemma":0.001252501,"domain_scores_codex":[0.9993844,0.0001636393,0.00007913941,0.000162789,0.0001023331,0.0001076617],"domain_scores_gemma":[0.9968145,0.00158969,0.0005570824,0.0003321983,0.0003482494,0.000358203],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00265393,0.0001695687,0.9788272,0.00005569879,0.0004159688,0.0002341304,0.0001109338,0.0006332721,0.003810437,0.000156369,0.0004478233,0.0124846],"study_design_scores_gemma":[0.00004367568,0.0006064378,0.9879106,0.0000311873,0.0004845703,0.001062909,0.000192887,0.006648276,0.001866781,0.0004684472,0.0006515494,0.00003267173],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956936,0.0004489579,0.001529949,0.00009530829,0.00004307121,0.00001799311,0.0003615841,0.00005645936,0.001752996],"genre_scores_gemma":[0.9989586,0.00004010403,0.0004443568,0.00002127388,0.00003611875,0.000009814588,0.0002192447,0.000008119093,0.0002624764],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002044958,"threshold_uncertainty_score":0.01081491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04648918762635706,"score_gpt":0.2984662089782217,"score_spread":0.2519770213518646,"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."}}