{"id":"W4281841850","doi":"10.1007/s12350-022-03012-6","title":"Machine learning to predict abnormal myocardial perfusion from pre-test features","year":2022,"lang":"en","type":"article","venue":"Journal of Nuclear Cardiology","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa; Libin Cardiovascular Institute of Alberta; University of Calgary","funders":"National Center for Advancing Translational Sciences; National Heart, Lung, and Blood Institute","keywords":"Medicine; Brier score; Myocardial perfusion imaging; Receiver operating characteristic; Population; Machine learning; Perfusion; Area under the curve; Internal medicine; Artificial intelligence; Cardiology; Computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.0005962998,0.0001544717,0.0007062366,0.0002180419,0.0002369327,0.00002197339,0.0001592885,0.00008221003,0.0002856676],"category_scores_gemma":[0.001399708,0.000136175,0.0005244021,0.0001485898,0.00005155802,0.00005320288,0.0002936376,0.001245558,0.00002863423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001563715,"about_ca_system_score_gemma":0.0001092907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006791585,"about_ca_topic_score_gemma":6.424947e-7,"domain_scores_codex":[0.9983884,0.0003011539,0.0003671444,0.0001931542,0.0004767856,0.000273391],"domain_scores_gemma":[0.9987316,0.0004602339,0.0001897587,0.0002093876,0.0001535358,0.0002554923],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002636719,0.0001491538,0.7406715,0.00001497754,0.0005919633,0.00329857,0.0018796,0.02365518,0.01314976,0.00002350027,0.2041483,0.009780806],"study_design_scores_gemma":[0.001616792,0.002334832,0.6028845,0.00002829325,0.0004070748,0.006870109,0.0005044601,0.0002573134,0.00002695064,0.00001973279,0.384927,0.000123015],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9860231,0.002452309,0.00008417817,0.00364678,0.002154829,0.0001729452,0.0001103286,0.00005896066,0.005296609],"genre_scores_gemma":[0.9943986,0.0001848735,0.0006042958,0.001761268,0.002722898,0.000002947252,0.00003257491,0.00004495919,0.0002475814],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1807787,"threshold_uncertainty_score":0.5553057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006672520652510888,"score_gpt":0.2387753079379712,"score_spread":0.2321027872854603,"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."}}