{"id":"W3204414146","doi":"10.1101/2020.08.09.243287","title":"Comparison of polygenic risk scores for coronary artery disease highlights obstacles to overcome for clinical use","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Montreal Heart Institute","funders":"","keywords":"Biobank; Missing data; Population; Framingham Risk Score; Medicine; Disease; Cohort; Risk assessment; Genetic association; Heritability; Demography; Computer science; Environmental health; Bioinformatics; Internal medicine; Biology; Machine learning; Genotype; Genetics","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1901487,0.00149228,0.002519524,0.002969565,0.0007824961,0.00666238,0.003405889,0.001582851,0.007779683],"category_scores_gemma":[0.4074262,0.0008795757,0.002789275,0.004856986,0.001888795,0.002682978,0.004457213,0.004354794,0.001434071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001269985,"about_ca_system_score_gemma":0.002099514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006034017,"about_ca_topic_score_gemma":0.004493942,"domain_scores_codex":[0.8005217,0.1790119,0.00586191,0.004992158,0.00888552,0.0007266827],"domain_scores_gemma":[0.5319785,0.3770119,0.0186327,0.04912848,0.0209231,0.002325455],"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":[0.003351126,0.0004801925,0.3994061,0.001558243,0.007423578,0.0003612364,0.003813247,0.0191268,0.001918793,0.0432984,0.02030424,0.4989579],"study_design_scores_gemma":[0.001059879,0.003438776,0.5310956,0.003260522,0.00178519,0.001505476,0.003941614,0.2000328,0.002952847,0.2053743,0.04487024,0.0006826713],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2956434,0.01131304,0.6354088,0.02554743,0.001731706,0.001716221,0.006492103,0.00192579,0.02022156],"genre_scores_gemma":[0.7919886,0.001883574,0.1988638,0.00193324,0.0006089265,0.0009403298,0.001818767,0.0004588526,0.001503934],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1901487,"threshold_uncertainty_score":0.9986908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06036530781546298,"score_gpt":0.330273216960265,"score_spread":0.269907909144802,"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."}}