{"id":"W4401066880","doi":"10.1093/europace/euae201","title":"Innovative approaches to atrial fibrillation prediction: should polygenic scores and machine learning be implemented in clinical practice?","year":2024,"lang":"en","type":"review","venue":"EP Europace","topic":"Atrial Fibrillation Management and Outcomes","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Montreal Heart Institute","funders":"Institut de Cardiologie de Montréal; Fondation Institut de Cardiologie de Montréal; Canada Research Chairs; Canadian Institute for Advanced Research; Österreichische Kardiologische Gesellschaft; Heart Rhythm Society","keywords":"Atrial fibrillation; Clinical Practice; Computer science; Machine learning; Artificial intelligence; Internal medicine; Medicine; Physical therapy","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":[0.01681742,0.0008713312,0.001814085,0.001389498,0.0003163307,0.003257146,0.001555717,0.002152878,0.004045556],"category_scores_gemma":[0.04726442,0.0003571529,0.001113899,0.001216428,0.001925335,0.003812034,0.001737557,0.004801285,0.001016322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001434313,"about_ca_system_score_gemma":0.004088293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004170357,"about_ca_topic_score_gemma":0.00479987,"domain_scores_codex":[0.9938746,0.004364727,0.0004461038,0.0004874293,0.000681748,0.0001454371],"domain_scores_gemma":[0.9715703,0.02197824,0.001259143,0.0008527176,0.003508718,0.0008308045],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0002494658,0.0001657609,0.01108676,0.004321805,0.0008209522,0.0001364403,0.0002833976,0.00379403,0.0002303451,0.02667248,0.0163704,0.9358682],"study_design_scores_gemma":[0.0005449269,0.001825199,0.03914382,0.04839025,0.002216283,0.001205495,0.001457095,0.04297315,0.001066323,0.4581724,0.4026213,0.0003836793],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.008221922,0.7212083,0.08137619,0.175054,0.003151319,0.0001911501,0.0004440251,0.0002923427,0.01006069],"genre_scores_gemma":[0.1577435,0.6801639,0.1151274,0.03550708,0.00761618,0.0007172409,0.0006270416,0.00009340121,0.002404183],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01681742,"threshold_uncertainty_score":0.08894008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5125238650941227,"score_gpt":0.4825758687372787,"score_spread":0.02994799635684403,"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."}}