{"id":"W4408169553","doi":"10.1101/2025.03.02.25322575","title":"Foundation models for generalizable electrocardiogram interpretation: comparison of supervised and self-supervised electrocardiogram foundation models","year":2025,"lang":"en","type":"preprint","venue":"medRxiv","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Jewish General Hospital; McGill University; Centre Hospitalier de l’Université de Montréal; Université de Montréal; Mila - Quebec Artificial Intelligence Institute; Montreal Heart Institute","funders":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; National Institutes of Health; Institut de Valorisation des Données; Canadian Institute for Advanced Research; American Heart Association","keywords":"Foundation (evidence); Interpretation (philosophy); Artificial intelligence; Computer science; Machine learning; Geography","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":[0.00510503,0.0009673173,0.0007939591,0.001011701,0.0003096968,0.0009845771,0.001745583,0.001062796,0.001547742],"category_scores_gemma":[0.01205503,0.0003522404,0.0009775516,0.0004477267,0.0006063279,0.001557875,0.001225056,0.001592751,0.0006517643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001238462,"about_ca_system_score_gemma":0.00177434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008345733,"about_ca_topic_score_gemma":0.01051247,"domain_scores_codex":[0.9987839,0.0005059246,0.00007501661,0.0003551474,0.0001713058,0.0001087618],"domain_scores_gemma":[0.9917049,0.005520132,0.0004940858,0.0008611616,0.00116082,0.0002589363],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001474741,0.0005994375,0.02859278,0.0002375732,0.000515646,0.0001941115,0.0002040699,0.6750854,0.002560748,0.004309065,0.01134395,0.2748824],"study_design_scores_gemma":[0.0000344509,0.00009114821,0.001367727,0.00002236878,0.00002690118,0.00003228637,0.00001285258,0.9951507,0.000676561,0.002248503,0.000326329,0.00001012344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4715678,0.002467066,0.5059196,0.001825722,0.0002364101,0.00033679,0.001929719,0.009298604,0.00641831],"genre_scores_gemma":[0.9386729,0.0003306818,0.05528357,0.0004715999,0.00009202538,0.0001322204,0.002736045,0.0002387833,0.00204224],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008345733,"threshold_uncertainty_score":0.02699834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03702498873035807,"score_gpt":0.3242361036201601,"score_spread":0.287211114889802,"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."}}