{"id":"W4388771197","doi":"10.3346/jkms.2023.38.e395","title":"Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review","year":2023,"lang":"en","type":"review","venue":"Journal of Korean Medical Science","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Queen's University","funders":"","keywords":"Artificial intelligence; Polygenic risk score; Machine learning; Disease; Computer science; Risk assessment; Framingham Risk Score; Curse of dimensionality; Medicine; Internal medicine; Biology","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":[0.001754724,0.0009013725,0.00117807,0.003469162,0.0002532599,0.001397014,0.001066095,0.0009057382,0.001922765],"category_scores_gemma":[0.004956496,0.0002726015,0.001237617,0.003353101,0.0005403825,0.001208963,0.0005473236,0.00104912,0.0004480289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007108827,"about_ca_system_score_gemma":0.001805959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003327677,"about_ca_topic_score_gemma":0.002532427,"domain_scores_codex":[0.9993798,0.0002277526,0.0001053201,0.0001084911,0.0001598208,0.0000187509],"domain_scores_gemma":[0.997622,0.001892206,0.0001598622,0.00005157314,0.0002356274,0.0000388453],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007336552,0.00009890815,0.006427265,0.01744652,0.0007358906,0.0002596644,0.0001671897,0.003472483,0.0003712846,0.01838528,0.01629346,0.9362687],"study_design_scores_gemma":[0.0001009624,0.0007248874,0.03937072,0.03271859,0.005227625,0.005899315,0.0006337243,0.0283798,0.001680934,0.1273731,0.7575273,0.0003631313],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001533057,0.9826026,0.01043224,0.002221375,0.0004047891,0.00004540902,0.0001871807,0.00005638168,0.00251688],"genre_scores_gemma":[0.01921007,0.9680082,0.01044595,0.0006287782,0.0006957654,0.00009827532,0.000291758,0.0000122816,0.0006089244],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003469162,"threshold_uncertainty_score":0.009280026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0841412510390364,"score_gpt":0.390410280346227,"score_spread":0.3062690293071906,"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."}}