{"id":"W4389242864","doi":"10.1136/heartjnl-2023-323296","title":"Incremental value of machine learning for risk prediction in tetralogy of Fallot","year":2023,"lang":"en","type":"article","venue":"Heart","topic":"Congenital Heart Disease Studies","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; Hospital for Sick Children; University of Toronto; University Health Network","funders":"Canadian Institutes of Health Research","keywords":"Medicine; Mace; Tetralogy of Fallot; Internal medicine; Cardiology; Ventricular tachycardia; Heart failure; Heart disease; Myocardial infarction; Percutaneous coronary intervention","routes":{"ca_aff":true,"ca_fund":true,"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":[0.02117767,0.001437998,0.001173527,0.00230231,0.0003277533,0.00167385,0.0008709494,0.001243557,0.001030865],"category_scores_gemma":[0.09114395,0.0003038966,0.0009689771,0.0008997507,0.0006880386,0.001657654,0.001272312,0.001553165,0.0003938471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007988803,"about_ca_system_score_gemma":0.001070932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001959071,"about_ca_topic_score_gemma":0.001571932,"domain_scores_codex":[0.9896242,0.006736769,0.0005743353,0.001048135,0.001570584,0.0004459947],"domain_scores_gemma":[0.8823288,0.1038047,0.004880248,0.002998787,0.004615351,0.001372057],"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":[0.002722897,0.0003909822,0.8833618,0.0001850468,0.001179945,0.0001956962,0.0001263129,0.04271214,0.0007155137,0.0003479058,0.001447056,0.06661475],"study_design_scores_gemma":[0.0002779492,0.002962367,0.2712772,0.0001928288,0.0009542108,0.001070548,0.0001560092,0.712029,0.003080154,0.006742631,0.00113587,0.000121248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9589875,0.004421665,0.02895822,0.001895695,0.0002451225,0.0002249188,0.0009417771,0.0004820824,0.003842936],"genre_scores_gemma":[0.9961073,0.0001703055,0.003091622,0.0001140422,0.0001032373,0.00002413146,0.0002716727,0.00001362623,0.0001040387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02117767,"threshold_uncertainty_score":0.1119995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02329632989104101,"score_gpt":0.3041397854022874,"score_spread":0.2808434555112463,"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."}}