{"id":"W4394369675","doi":"10.6084/m9.figshare.14068007","title":"Additional file 2 of Development of genome-wide polygenic risk scores for lipid traits and clinical applications for dyslipidemia, subclinical atherosclerosis, and diabetes cardiovascular complications among East Asians","year":2021,"lang":"en","type":"dataset","venue":"Open MIND","topic":"Lipoproteins and Cardiovascular Health","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre","funders":"","keywords":"Dyslipidemia; Subclinical infection; Polygenic risk score; Diabetes mellitus; Medicine; Internal medicine; Bioinformatics; Biology; Genetics; Gene; Endocrinology; Genotype; Single-nucleotide polymorphism","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00350347,0.001583879,0.001445946,0.002044229,0.001165129,0.001919805,0.002447078,0.001460511,0.779981],"category_scores_gemma":[0.04761972,0.0007598342,0.00158302,0.0033923,0.0002954587,0.001725734,0.001160974,0.001178404,0.09774101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009322269,"about_ca_system_score_gemma":0.002211423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01187239,"about_ca_topic_score_gemma":0.02017228,"domain_scores_codex":[0.9987822,0.0003252576,0.0002217493,0.0003305063,0.000194399,0.0001459269],"domain_scores_gemma":[0.9732588,0.02053801,0.001191217,0.001660715,0.002738918,0.0006123253],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006730336,0.0001742412,0.01258696,0.002697564,0.0001696205,0.0002202529,0.0001447197,0.00070682,0.0001751871,0.001005187,0.9669137,0.01453265],"study_design_scores_gemma":[0.01904157,0.001089659,0.1419865,0.007169804,0.001292026,0.001843835,0.001386441,0.01011083,0.002294941,0.02504012,0.7882451,0.0004992258],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005098799,0.00002273804,0.0007876606,0.0001386162,0.00003224845,0.0001986416,0.9973035,0.0003016798,0.0007050619],"genre_scores_gemma":[0.01839047,0.0001747923,0.01160063,0.0009763848,0.000177884,0.005862523,0.9518194,0.001155775,0.009842112],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.779981,"threshold_uncertainty_score":0.3138304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04887513851882973,"score_gpt":0.3175056562305992,"score_spread":0.2686305177117694,"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."}}