{"id":"W2744211371","doi":"10.1186/s12916-017-0921-6","title":"Circulating tumor DNA for personalized lung cancer monitoring","year":2017,"lang":"en","type":"letter","venue":"BMC Medicine","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; University of Toronto; Mount Sinai Hospital","funders":"","keywords":"Medicine; Personalized medicine; Circulating tumor DNA; Lung cancer; Precision medicine; Liquid biopsy; DNA sequencing; Cancer; Computational biology; Deep sequencing; Bioinformatics; Internal medicine; DNA; Pathology; Genome; Gene; Genetics; Biology","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"],"consensus_categories":[],"category_scores_codex":[0.0002105659,0.0003233087,0.0004521479,0.00005254645,0.000199007,0.00003629009,0.0003859097,0.0003902231,0.00005537401],"category_scores_gemma":[0.0008298374,0.0002920706,0.0001818369,0.0000225118,0.0001493703,0.000001818236,0.0001012566,0.0003173313,0.000001508918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008014048,"about_ca_system_score_gemma":0.000406628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002313235,"about_ca_topic_score_gemma":0.00003489502,"domain_scores_codex":[0.998455,0.00002463088,0.0003037581,0.0005898239,0.0002178078,0.0004089816],"domain_scores_gemma":[0.9985835,0.0001255053,0.0003666037,0.0006758719,0.0001667588,0.00008177096],"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":[0.00004738153,0.000005395053,0.003183145,0.0006911104,0.0001293977,0.0000594365,0.00005622098,0.00001548725,0.02328487,0.000009744138,0.9720084,0.0005093914],"study_design_scores_gemma":[0.00166017,0.0001263123,0.0003615864,0.000691783,0.0002886796,0.00003351356,0.0000287373,0.0001467799,0.002754305,0.00003295486,0.9934861,0.0003890617],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.17727,0.1766017,0.01993456,0.5743511,0.03472184,0.006507365,0.002332334,0.0001518038,0.008129266],"genre_scores_gemma":[0.05945424,0.005264725,0.005137422,0.4539365,0.4225217,0.002550425,0.005886726,0.0007297868,0.0445184],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.3877999,"threshold_uncertainty_score":0.9999532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03732327297593449,"score_gpt":0.3342828127550446,"score_spread":0.29695953977911,"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."}}