{"id":"W4221019445","doi":"10.1038/s41431-022-01085-y","title":"Correction: Polygenic risk modeling for prediction of epithelial ovarian cancer risk","year":2022,"lang":"en","type":"erratum","venue":"European Journal of Human Genetics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Université Laval; Royal Alexandra Hospital; Public Health Ontario; University of British Columbia; Alberta Health Services; Centre hospitalier universitaire de Québec; Mount Sinai Hospital; Lunenfeld-Tanenbaum Research Institute; Vancouver General Hospital; Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"National Institute of General Medical Sciences; National Cancer Institute; Medical Research Council","keywords":"Ovarian cancer; Polygenic risk score; Computational biology; Biology; Bioinformatics; Medicine; Oncology; Genetics; Cancer; Gene; Single-nucleotide polymorphism; Genotype","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":[0.0101995,0.002819418,0.0028387,0.004381931,0.002351816,0.004412411,0.005522009,0.004766176,0.2378964],"category_scores_gemma":[0.1904155,0.001355848,0.003334591,0.005720407,0.001218868,0.003573162,0.002834388,0.006206675,0.0556527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002207453,"about_ca_system_score_gemma":0.007299101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02126163,"about_ca_topic_score_gemma":0.01941668,"domain_scores_codex":[0.9939779,0.002296336,0.0009801193,0.001040825,0.001232411,0.0004723713],"domain_scores_gemma":[0.9415382,0.02807961,0.002416809,0.005339252,0.02083936,0.001786798],"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":[0.0001005417,0.000006708759,0.0006873996,0.0004893928,0.0001111935,0.0001820904,0.00005941328,0.0002710873,0.0000399433,0.001183358,0.9859712,0.01089787],"study_design_scores_gemma":[0.0007346245,0.00008178756,0.007592495,0.002921099,0.0005664143,0.001963664,0.0003741092,0.006374775,0.0005924366,0.01407832,0.9645612,0.0001590535],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.00116562,0.002972125,0.01181686,0.05332214,0.8880001,0.0001581888,0.03520609,0.002600973,0.004757983],"genre_scores_gemma":[0.1359638,0.01380023,0.06061043,0.08112308,0.3104943,0.002689222,0.05961982,0.01218718,0.3235119],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.2378964,"threshold_uncertainty_score":0.7958429,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.103885925566049,"score_gpt":0.3578293439543468,"score_spread":0.2539434183882978,"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."}}