{"id":"W4381331132","doi":"10.1161/circimaging.122.015205","title":"Machine Learning for Prediction of Adverse Cardiovascular Events in Adults With Repaired Tetralogy of Fallot Using Clinical and Cardiovascular Magnetic Resonance Imaging Variables","year":2023,"lang":"en","type":"article","venue":"Circulation Cardiovascular Imaging","topic":"Congenital Heart Disease Studies","field":"Medicine","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; University of Toronto; University Health Network","funders":"","keywords":"Tetralogy of Fallot; Mace; Ejection fraction; Magnetic resonance imaging; Medicine; Cardiology; Internal medicine; Random forest; Heart failure; Machine learning; Artificial intelligence; Algorithm; Heart disease; Radiology; Computer science; Percutaneous coronary intervention; Myocardial infarction","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":[0.01175994,0.0007784868,0.0009380449,0.001629325,0.000324693,0.0008512788,0.0008248333,0.0006499747,0.0006573463],"category_scores_gemma":[0.02632702,0.0002574577,0.0009915455,0.0007621955,0.0003757721,0.0006842388,0.0007117881,0.001241672,0.0002445961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008653718,"about_ca_system_score_gemma":0.00161624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00491951,"about_ca_topic_score_gemma":0.003168501,"domain_scores_codex":[0.9972476,0.001824123,0.0001825584,0.0003940329,0.0002301991,0.0001213839],"domain_scores_gemma":[0.9832711,0.01354282,0.001287742,0.0006088441,0.0009583364,0.0003312502],"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":[0.001767577,0.001005382,0.7084329,0.0001261713,0.001241462,0.0001565435,0.0001562969,0.196945,0.0005811567,0.0004604121,0.001837816,0.08728923],"study_design_scores_gemma":[0.0001163399,0.0007689024,0.05401497,0.00004946626,0.0001627595,0.0001389071,0.000046309,0.9425585,0.0004562709,0.001401476,0.0002620121,0.00002417232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9605538,0.001199894,0.03560877,0.0008773216,0.00006601139,0.0001665277,0.000502233,0.0002097048,0.0008157615],"genre_scores_gemma":[0.9890522,0.0001883637,0.009998378,0.00008414148,0.00003505596,0.00007597418,0.0004248504,0.000007843384,0.0001332079],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01175994,"threshold_uncertainty_score":0.06219321,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02337386709667038,"score_gpt":0.2663600142399909,"score_spread":0.2429861471433206,"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."}}