{"id":"W3083153841","doi":"10.1038/s41591-020-1078-y","title":"Author Correction: Detection of renal cell carcinoma using plasma and urine cell-free DNA methylomes","year":2020,"lang":"en","type":"erratum","venue":"Nature Medicine","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; BC Children's Hospital; University Health Network; University of British Columbia","funders":"National Cancer Institute; Dana-Farber Cancer Institute; Dana-Farber/Harvard Cancer Center; U.S. Department of Defense","keywords":"Renal cell carcinoma; Cell-free fetal DNA; DNA; Urine; Cell; Plasma cell; Cancer research; Computational biology; Computer science; Chemistry; Biology; Medicine; Oncology; Internal medicine; Genetics; Biochemistry; Antibody; Fetus; Prenatal diagnosis","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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0001576163,0.000346504,0.0005212135,0.0001380459,0.00007630479,0.00001055578,0.0002420087,0.001351657,0.00002458145],"category_scores_gemma":[0.0005101989,0.0003111137,0.0001037392,0.0001894723,0.0001545,0.000002163976,0.000238474,0.00117779,6.634771e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005051116,"about_ca_system_score_gemma":0.0002544776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001401435,"about_ca_topic_score_gemma":0.0001767094,"domain_scores_codex":[0.9985232,0.00004884745,0.000366495,0.0005568945,0.000286202,0.000218297],"domain_scores_gemma":[0.9987524,0.00005349561,0.0003557706,0.0004743421,0.0001973671,0.0001666344],"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":[0.0001531959,0.00002125566,0.00004330285,0.0002739841,0.00003827532,0.00001549136,0.00005749275,0.00001351632,0.498525,0.000002085824,0.4999157,0.0009406465],"study_design_scores_gemma":[0.001161928,0.0009908673,0.0002877852,0.0000915507,0.0002918778,0.00004605146,0.00006958376,0.0004679836,0.4730703,0.00001999025,0.5232584,0.0002437169],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5789164,0.232164,0.002627343,0.003586275,0.1543388,0.001302647,0.0005992521,0.00006106702,0.02640433],"genre_scores_gemma":[0.9432967,0.003248021,0.000487523,0.0005512647,0.01377482,0.00001200113,0.0009875562,0.0001035641,0.03753855],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3643804,"threshold_uncertainty_score":0.9999448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008086949005478242,"score_gpt":0.244018055989309,"score_spread":0.2359311069838308,"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."}}