{"id":"W2981946351","doi":"10.1093/eurheartj/ehz747.0001","title":"29Prognostic safety of automatic cancellation of rest myocardial perfusion scan by machine learning: a report from multicenter REFINE SPECT registry of new generation SPECT","year":2019,"lang":"en","type":"article","venue":"European Heart Journal","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Medicine; SSS*; Myocardial perfusion imaging; Receiver operating characteristic; Revascularization; Myocardial infarction; Population; Nuclear medicine; Perfusion scanning; Cutoff; Stress testing (software); Perfusion; Radiology; Artificial intelligence; Internal medicine; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007774619,0.0003374471,0.0006053287,0.0007857636,0.0002412604,0.0007621544,0.0007321645,0.0004832137,0.0006689433],"category_scores_gemma":[0.01839102,0.0001882253,0.0003683801,0.0008685382,0.0004232809,0.0004713968,0.0005018603,0.0003162873,0.0003116353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004726327,"about_ca_system_score_gemma":0.0006158122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002884649,"about_ca_topic_score_gemma":0.001623169,"domain_scores_codex":[0.9939322,0.003175886,0.0006021938,0.0006801513,0.001301739,0.000307957],"domain_scores_gemma":[0.9757369,0.008149251,0.007403003,0.003460897,0.004775357,0.0004746519],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007051474,0.00005131495,0.9895991,0.00002049972,0.00005026228,0.0001615678,0.00004273579,0.0003162515,0.0006924652,0.00002795301,0.0002995246,0.008033199],"study_design_scores_gemma":[0.00008429298,0.001387541,0.9879034,0.00001225283,0.0001136109,0.0009285853,0.00008703527,0.005765637,0.002619151,0.00005941259,0.001019721,0.0000192216],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971212,0.0002814995,0.001044581,0.00006351834,0.000005455616,0.00004024029,0.000842999,0.00005041355,0.0005501292],"genre_scores_gemma":[0.9979031,0.0000766665,0.0004133617,0.00004498935,0.00001543328,0.00003388743,0.001428353,0.00001371912,0.00007045244],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007774619,"threshold_uncertainty_score":0.0411166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01863755320107973,"score_gpt":0.2818984532114607,"score_spread":0.2632609000103809,"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."}}