{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001698773,0.0005045502,0.0006618394,0.0005397998,0.0006906047,0.00134525,0.0006435335,0.008180885,0.00303764],"category_scores_gemma":[0.009503695,0.0002535037,0.0005977448,0.0002992452,0.001562399,0.001512698,0.0006556929,0.009852428,0.003899008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001732379,"about_ca_system_score_gemma":0.0008167622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005164217,"about_ca_topic_score_gemma":0.0007175116,"domain_scores_codex":[0.998833,0.0003936384,0.000108657,0.0001641406,0.000401876,0.00009870331],"domain_scores_gemma":[0.9961539,0.002685682,0.0002064261,0.0001885136,0.0005535519,0.0002120215],"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":[0.000389089,0.0001164607,0.003899377,0.0006595069,0.00006305466,0.0136703,0.0001955774,0.0005636619,0.01028959,0.01584903,0.7034238,0.2508806],"study_design_scores_gemma":[0.0002084591,0.0002527366,0.002622408,0.0007448153,0.00007643642,0.02194442,0.0001824164,0.002613612,0.008285965,0.03524982,0.9277572,0.00006184309],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.004266002,0.05912779,0.005448272,0.8865644,0.03408544,0.00009540899,0.0001801923,0.0003158281,0.009916667],"genre_scores_gemma":[0.09000908,0.06747053,0.008562878,0.6606372,0.158964,0.0002801552,0.0002663683,0.0001271539,0.01368263],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.008180885,"threshold_uncertainty_score":0.01256937,"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."}}