{"id":"W2947485117","doi":"10.1186/s13073-019-0634-x","title":"Designing circulating tumor DNA-based interventional clinical trials in oncology","year":2019,"lang":"en","type":"review","venue":"Genome Medicine","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto","funders":"Genentech; Astellas Pharma; Regeneron Pharmaceuticals; Symphogen; Bristol-Myers Squibb; AstraZeneca; American Society of Clinical Oncology; GlaxoSmithKline; Celgene; Amgen; Pfizer; Agios Pharmaceuticals; Conquer Cancer Foundation","keywords":"Circulating tumor DNA; Medicine; Clinical trial; Minimal residual disease; Oncology; Snapshot (computer storage); Clinical Oncology; Precision oncology; Bioinformatics; Internal medicine; Computational biology; Medical physics; Cancer; Computer science; 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":[0.009339792,0.0003916614,0.003358567,0.0002455712,0.00003442244,0.00001365022,0.0003860646,0.0005101973,0.0002169903],"category_scores_gemma":[0.006927795,0.0003182966,0.0009401094,0.0001776881,0.0001487656,0.000001351832,0.0001702269,0.000462176,0.00005227427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001892802,"about_ca_system_score_gemma":0.001544112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002022957,"about_ca_topic_score_gemma":0.00003493922,"domain_scores_codex":[0.9942949,0.001602674,0.002833266,0.0007282312,0.0001784265,0.0003624339],"domain_scores_gemma":[0.9960244,0.001864058,0.001359622,0.0005163179,0.0000819754,0.0001536083],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000100427,0.0002503527,0.0002242198,0.00673756,0.0004095037,0.0001411963,0.00002506504,0.000101072,0.0008283014,0.000055684,0.002286114,0.9888405],"study_design_scores_gemma":[0.001789568,0.001162389,0.00006200013,0.004476693,0.0005956052,0.00004471549,0.0000207443,0.00002022781,0.000008485778,0.00002553328,0.99148,0.0003140247],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001988758,0.9936225,0.002862471,0.0001054832,0.001406291,0.00104945,0.00008040743,0.000007639466,0.00066692],"genre_scores_gemma":[0.0005881261,0.9924223,0.0008919612,0.0006138501,0.003293176,0.0001356362,0.001815156,0.00007461567,0.0001651704],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9891939,"threshold_uncertainty_score":0.9999269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.295144265686941,"score_gpt":0.5026001751229876,"score_spread":0.2074559094360466,"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."}}