{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003024378,0.00006535162,0.0001346409,0.00002534117,0.0001096356,0.0000227031,0.0000516597,0.00002912374,0.00001067851],"category_scores_gemma":[0.0003933194,0.00005655961,0.0003086947,0.00002805007,0.00004859565,0.000003269328,0.00005084939,0.0001096055,0.000001633587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004428384,"about_ca_system_score_gemma":0.00007464721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001016649,"about_ca_topic_score_gemma":0.000003854557,"domain_scores_codex":[0.9986979,0.0005612844,0.0003369796,0.0001958239,0.00007160943,0.0001364455],"domain_scores_gemma":[0.9995447,0.00006545849,0.0000796174,0.0001436536,0.00005611648,0.0001104364],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002382895,0.00000968838,0.9622717,0.00002192032,0.0003407462,0.000002188988,0.00003019128,0.00005229955,0.0005196444,0.000005429984,0.003465757,0.03325664],"study_design_scores_gemma":[0.0002994511,0.0001823316,0.9639449,0.00001453008,0.0001579692,0.00002625575,0.0000738546,0.0005691272,0.00007902608,0.00008636089,0.03451249,0.00005372703],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9780694,0.01125442,0.009993624,0.0001432393,0.0002917708,0.0001186704,0.00007285542,0.00000566912,0.00005033262],"genre_scores_gemma":[0.9953223,0.001866554,0.001937884,0.00004744238,0.0007579548,0.000002252109,0.00002215973,0.00001420452,0.00002921132],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03320292,"threshold_uncertainty_score":0.2306434,"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."}}