{"id":"W4407150135","doi":"10.1093/eurheartj/ehae649","title":"Clinical utility and implementation of polygenic risk scores for predicting cardiovascular disease","year":2024,"lang":"en","type":"article","venue":"European Heart Journal","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"NIHR Cambridge Biomedical Research Centre; Economic and Social Research Council; Chief Scientist Office, Scottish Government Health and Social Care Directorate; Department of Health and Social Care; Engineering and Physical Sciences Research Council; Cambridge BHF Centre of Research Excellence; Health and Social Care Research and Development Division; National Institute for Health and Care Research; Scottish Government; British Heart Foundation; Wellcome Trust; Medical Research Council; Public Health Agency; Wellcome","keywords":"Medicine; Disease; Metric (unit); Clinical Practice; Personalized medicine; MEDLINE; Health care; Risk analysis (engineering); Intensive care medicine; Bioinformatics; Family medicine; Pathology","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":[0.1082952,0.001232323,0.001282334,0.004044642,0.0007266187,0.007432934,0.002453244,0.002770502,0.003156658],"category_scores_gemma":[0.2404911,0.000631612,0.001511813,0.002862612,0.002169696,0.003318379,0.003166303,0.00483893,0.001343946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00150467,"about_ca_system_score_gemma":0.004962971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005970593,"about_ca_topic_score_gemma":0.006228194,"domain_scores_codex":[0.9268993,0.05339369,0.004814209,0.003446853,0.01063573,0.0008103327],"domain_scores_gemma":[0.8003457,0.1449675,0.01016073,0.01360663,0.02827765,0.00264175],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0006911515,0.0002262138,0.1910839,0.001332822,0.001141451,0.000217298,0.001243138,0.003517829,0.0004815392,0.03060472,0.02763954,0.7418204],"study_design_scores_gemma":[0.0008913841,0.003791111,0.4022885,0.01960524,0.00416939,0.002859376,0.003421973,0.07533637,0.004585819,0.2769773,0.2051482,0.0009253414],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1601485,0.1524704,0.3093822,0.2622489,0.006426515,0.001500557,0.006825107,0.00182047,0.09917725],"genre_scores_gemma":[0.6765355,0.0466931,0.2539956,0.01305823,0.003300477,0.000759036,0.002548385,0.0002810953,0.002828622],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1082952,"threshold_uncertainty_score":0.5727264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0424087234771149,"score_gpt":0.366399317796491,"score_spread":0.3239905943193761,"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."}}