{"id":"W4383265571","doi":"10.1016/j.jcct.2023.06.004","title":"Accuracy of a virtual PCI planner based on coronary CT angiography in calcific lesions","year":2023,"lang":"en","type":"letter","venue":"Journal of cardiovascular computed tomography","topic":"Coronary Interventions and Diagnostics","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Medicine; Fractional flow reserve; Conventional PCI; Percutaneous coronary intervention; Coronary artery disease; Cardiology; Revascularization; Internal medicine; Angina; Radiology; Coronary angiography; Myocardial infarction","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":[0.0009334605,0.0003054008,0.0004859014,0.0007703475,0.0003450824,0.001330109,0.0004778791,0.001470041,0.003030546],"category_scores_gemma":[0.01315599,0.0003162394,0.0005218349,0.0003445891,0.0003629082,0.0005003068,0.000478303,0.001032928,0.001070339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007321381,"about_ca_system_score_gemma":0.0005955235,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003508658,"about_ca_topic_score_gemma":0.00222674,"domain_scores_codex":[0.9994974,0.0002196014,0.00005208876,0.00005805885,0.0001339997,0.0000388345],"domain_scores_gemma":[0.9952727,0.003518586,0.0001528509,0.0004252836,0.0004732912,0.0001573791],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0105973,0.0002477203,0.1073577,0.0005877825,0.0001987251,0.02147297,0.0007119382,0.1030437,0.0383754,0.005959701,0.04171395,0.6697332],"study_design_scores_gemma":[0.0007000261,0.002269618,0.0799174,0.0004458097,0.0008390716,0.06474444,0.0006547594,0.7362035,0.04820279,0.01497238,0.05071595,0.0003343326],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"commentary","genre_scores_codex":[0.6398864,0.01147963,0.2608569,0.03063458,0.004416515,0.0002211077,0.001002748,0.004368551,0.04713351],"genre_scores_gemma":[0.9447911,0.001200444,0.04995909,0.001544173,0.0005256149,0.00005792899,0.0002445997,0.0001657992,0.0015113],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.003508658,"threshold_uncertainty_score":0.01013815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02427681988110678,"score_gpt":0.2640879743438639,"score_spread":0.2398111544627571,"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."}}