{"id":"W4324136732","doi":"10.1200/jco.2023.41.6_suppl.232","title":"Machine-learning to predict utility of circulating tumor DNA (ctDNA) for somatic genotyping.","year":2023,"lang":"en","type":"article","venue":"Journal of Clinical Oncology","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Canadian Cancer Society Research Institute; Canadian Institutes of Health Research","keywords":"Medicine; Receiver operating characteristic; Cell-free fetal DNA; Genotyping; Prostate cancer; Internal medicine; Oncology; Area under the curve; Cancer; Genotype; Biology; Genetics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003165878,0.000977921,0.0006869723,0.001112429,0.0002731385,0.0007851502,0.0007841229,0.001018979,0.001358685],"category_scores_gemma":[0.005325548,0.0002886114,0.000739445,0.0004756396,0.0003948042,0.0005101825,0.000546862,0.001210541,0.001028541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007570249,"about_ca_system_score_gemma":0.0009697145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004199688,"about_ca_topic_score_gemma":0.00282014,"domain_scores_codex":[0.9993875,0.0002476387,0.0000467042,0.0001578977,0.00007494073,0.00008532029],"domain_scores_gemma":[0.9969445,0.002244537,0.0002930319,0.0001128932,0.0002870059,0.000117934],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001225326,0.0008824447,0.2722083,0.0001703287,0.0005460429,0.0001826814,0.0001224223,0.5257481,0.004343912,0.0006376617,0.004492413,0.1894404],"study_design_scores_gemma":[0.00001904087,0.0001890397,0.01186251,0.00001808926,0.00002798862,0.00006751412,0.00001646097,0.9854269,0.001165941,0.0009036947,0.0002914065,0.00001143876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8736947,0.002039656,0.1173564,0.001089288,0.0001356313,0.0001896218,0.001747892,0.001336815,0.002410049],"genre_scores_gemma":[0.9833545,0.0001247183,0.01411929,0.0001296072,0.00003393106,0.000091015,0.001085714,0.00002826222,0.001032971],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004199688,"threshold_uncertainty_score":0.01674294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07925642358073769,"score_gpt":0.4134002072848622,"score_spread":0.3341437837041245,"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."}}