{"id":"W4402994527","doi":"10.1161/circimaging.124.016958","title":"Patient-Specific Myocardial Infarction Risk Thresholds From AI-Enabled Coronary Plaque Analysis","year":2024,"lang":"en","type":"article","venue":"Circulation Cardiovascular Imaging","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Siemens Healthineers; British Heart Foundation; InfraRedx; Anthera; Amarin Corporation; Esperion Therapeutics; Cedars-Sinai Medical Center; Wellcome Trust; Eli Lilly and Company; AstraZeneca; CSL Behring; Regeneron Pharmaceuticals; Amgen; Nihon University; Medicines Company; Society of Nuclear Medicine and Molecular Imaging; Dr. Miriam and Sheldon G. Adelson Medical Research Foundation; Silence Therapeutics; Fundacja na rzecz Nauki Polskiej; Sanofi; National Heart, Lung, and Blood Institute; Pfizer","keywords":"Medicine; Myocardial infarction; Cardiology; Internal medicine; Coronary artery disease; Percentile; Computed tomography angiography; Radiology; Hazard ratio; Infarction; Vulnerable plaque; Angiography; Confidence interval","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":[0.001777388,0.0005059574,0.0004515344,0.001357159,0.0001654126,0.001281642,0.0004481394,0.0005702858,0.001899533],"category_scores_gemma":[0.007131537,0.0001941113,0.0004811585,0.0005809541,0.0002279744,0.0003858921,0.0007151285,0.0006503078,0.000436309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003210008,"about_ca_system_score_gemma":0.0002854673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008502399,"about_ca_topic_score_gemma":0.001309537,"domain_scores_codex":[0.999379,0.0001940316,0.00006940011,0.000158553,0.0001200117,0.00007896409],"domain_scores_gemma":[0.9973873,0.00131315,0.0005699508,0.0002421028,0.0002929991,0.0001944983],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001429869,0.0001067943,0.962156,0.00005498896,0.0002293413,0.0001538914,0.00009193984,0.005244425,0.002814488,0.0004148143,0.0007988839,0.02650453],"study_design_scores_gemma":[0.00009310113,0.000479562,0.9149461,0.00005487182,0.0003825811,0.0009585036,0.0001513367,0.07295617,0.005353119,0.00327398,0.001296804,0.00005365124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9909993,0.0004319029,0.005129472,0.0001482528,0.00001439482,0.00002897353,0.001271207,0.00009847436,0.001878039],"genre_scores_gemma":[0.9969512,0.00007592255,0.0018816,0.00004256469,0.00001397107,0.00001939646,0.0008494729,0.000009128505,0.0001566454],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001899533,"threshold_uncertainty_score":0.009399772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008355041493322475,"score_gpt":0.2340737379003403,"score_spread":0.2257186964070178,"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."}}