{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003952037,0.0009160496,0.001619271,0.001911453,0.0002730063,0.001788194,0.001429962,0.002760838,0.003493632],"category_scores_gemma":[0.004327714,0.0004647789,0.0007320522,0.001466373,0.0009685142,0.001913843,0.001040256,0.003447378,0.002151185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001226441,"about_ca_system_score_gemma":0.001482269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005568182,"about_ca_topic_score_gemma":0.001131083,"domain_scores_codex":[0.9991867,0.0003527792,0.00008805772,0.0001136938,0.0001969626,0.00006177297],"domain_scores_gemma":[0.9972358,0.002109556,0.0002259902,0.00005080293,0.0002677503,0.0001101284],"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":[0.000123568,0.0001076731,0.0001615308,0.01654838,0.0001198095,0.0002174399,0.00006077934,0.0006592225,0.00213204,0.01170204,0.01979143,0.9483761],"study_design_scores_gemma":[0.0001183253,0.000277625,0.0006091134,0.01087558,0.0002041569,0.001044476,0.0000721439,0.0002980544,0.001168317,0.01092826,0.9743659,0.0000379907],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"methods","genre_scores_codex":[0.0001033965,0.9970229,0.0008091881,0.0007430019,0.0002993375,0.00002673437,0.00002251139,0.00001180677,0.0009610176],"genre_scores_gemma":[0.001240079,0.9952403,0.001422498,0.001022358,0.0003363375,0.00007025479,0.00004953551,0.000005197849,0.0006133868],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.003952037,"threshold_uncertainty_score":0.02090067,"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."}}