{"id":"W4402112527","doi":"10.1093/eurheartj/ehae595","title":"Prediction of incident atrial fibrillation using deep learning, clinical models, and polygenic scores","year":2024,"lang":"en","type":"article","venue":"European Heart Journal","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; Université de Montréal; Montreal Heart Institute","funders":"Canadian Institutes of Health Research; Takeda Canada; Institute of Genetics; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Medicine; Atrial fibrillation; Internal medicine; Cardiology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.002891001,0.0007266187,0.0004399061,0.001220234,0.0001916326,0.0009226789,0.000488605,0.0005359713,0.0008437724],"category_scores_gemma":[0.006800874,0.0001747367,0.000583794,0.0005570225,0.0003272678,0.000504408,0.0007953103,0.0009092344,0.0002034638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006550142,"about_ca_system_score_gemma":0.0007146809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005996975,"about_ca_topic_score_gemma":0.005084003,"domain_scores_codex":[0.9992279,0.0003408445,0.00006156215,0.0001803772,0.00008964932,0.00009958486],"domain_scores_gemma":[0.9965914,0.002247804,0.0004233177,0.0002018966,0.0002959187,0.0002396679],"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":[0.000803504,0.0006130622,0.716699,0.00006213826,0.0005427005,0.0001990883,0.00008544174,0.1969658,0.000830697,0.0004166692,0.001403689,0.08137825],"study_design_scores_gemma":[0.00005315972,0.0003305626,0.06398012,0.00003015461,0.00009975102,0.000145296,0.00003429256,0.9326567,0.0004240223,0.001997116,0.0002321095,0.00001660291],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9735994,0.0005248534,0.0232085,0.0007229957,0.00002874404,0.00004264912,0.0006949164,0.0001412285,0.001036685],"genre_scores_gemma":[0.9957074,0.00008655046,0.003295118,0.00006607181,0.00001860406,0.00001771866,0.0006133777,0.000003872162,0.0001912431],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005996975,"threshold_uncertainty_score":0.01528925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1400441098688014,"score_gpt":0.3785306211130712,"score_spread":0.2384865112442698,"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."}}