{"id":"W4313489242","doi":"10.1007/s12350-022-03196-x","title":"Correction to: Integration of coronary artery calcium scoring from CT attenuation scans by machine learning improves prediction of adverse cardiovascular events in patients undergoing SPECT/CT myocardial perfusion imaging","year":2023,"lang":"en","type":"erratum","venue":"Journal of Nuclear Cardiology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"National Heart, Lung, and Blood Institute","keywords":"Medicine; Myocardial perfusion imaging; Correction for attenuation; Perfusion; Perfusion scanning; Coronary artery calcium; Radiology; Adverse effect; Cardiology; Coronary artery disease; Internal medicine; Nuclear medicine; Positron emission tomography","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":[0.001461221,0.0003507248,0.001683045,0.0008975443,0.00009602447,0.000008155468,0.0001884715,0.0001154837,0.00001345197],"category_scores_gemma":[0.002106388,0.0003328408,0.0009508231,0.0003618182,0.00009451144,0.000208685,0.0001323925,0.002466974,0.000005739026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00070547,"about_ca_system_score_gemma":0.000262781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009049748,"about_ca_topic_score_gemma":0.00001642156,"domain_scores_codex":[0.99631,0.0006396374,0.00135679,0.0004472942,0.0009145048,0.0003317652],"domain_scores_gemma":[0.997777,0.0002539566,0.001145841,0.0003063553,0.0003507735,0.0001660213],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001597925,0.0002160069,0.7704625,0.0003301472,0.002850535,0.0007665006,0.0008785528,0.02129113,0.0230216,0.000001122472,0.09935445,0.07922954],"study_design_scores_gemma":[0.005016011,0.001799645,0.9255303,0.004956994,0.001679391,0.001054147,0.00105326,0.05223672,0.0001396386,0.00001911106,0.006178505,0.0003363287],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.963355,0.001034297,0.002665368,0.0001986627,0.03167799,0.0004865409,0.00006858168,0.00004439796,0.0004691676],"genre_scores_gemma":[0.9952949,0.001427403,0.000176838,0.0000689132,0.001776834,0.000003763084,0.0007337006,0.000124254,0.0003933928],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1550678,"threshold_uncertainty_score":0.9999124,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009687535748003163,"score_gpt":0.2418681542144526,"score_spread":0.2321806184664494,"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."}}