{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006318198,0.0003427209,0.0008428077,0.000775094,0.0002591572,0.0003520863,0.00007381573,0.0001287772,0.0001543392],"category_scores_gemma":[0.000170365,0.0003578383,0.003146025,0.001506794,0.0001010687,0.0004504187,0.0000703534,0.0005693449,0.0002045405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003951173,"about_ca_system_score_gemma":0.0001647618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007007426,"about_ca_topic_score_gemma":0.000003036177,"domain_scores_codex":[0.9969069,0.0002494536,0.0005138215,0.0009152835,0.001006462,0.0004080892],"domain_scores_gemma":[0.9980429,0.0002551722,0.00007615954,0.001110084,0.0002996815,0.0002159452],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004192888,0.0000363619,0.8488494,0.00003920238,0.01435528,0.0004922793,0.0005048898,0.06005482,0.0004078846,0.00006647343,0.003262021,0.07188942],"study_design_scores_gemma":[0.001025292,0.00001244918,0.8635061,0.0001262638,0.01404815,0.0002314852,0.0001945341,0.05521021,0.0001405495,0.0003412711,0.06476482,0.0003988122],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6690264,0.08612328,0.2360558,0.0007374612,0.002932329,0.0007694218,0.0002282715,0.0009538402,0.003173271],"genre_scores_gemma":[0.9956428,0.000888241,0.0005212393,0.0003860932,0.001224787,0.00005175805,0.001187854,0.00008333011,0.00001392762],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3266164,"threshold_uncertainty_score":0.9998873,"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."}}