{"id":"W3118553886","doi":"10.1093/clinchem/hvaa307","title":"Giant Magnetoresistive Nanosensor Analysis of Circulating Tumor DNA Epidermal Growth Factor Receptor Mutations for Diagnosis and Therapy Response Monitoring","year":2020,"lang":"en","type":"article","venue":"Clinical Chemistry","topic":"Lung Cancer Treatments and Mutations","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Cancer Institute; National Institute of General Medical Sciences; Merck; Pfizer; U.S. Department of Veterans Affairs","keywords":"Liquid biopsy; Epidermal growth factor receptor; COLD-PCR; Concordance; Medicine; T790M; Internal medicine; Digital polymerase chain reaction; Targeted therapy; Mutation; Molecular biology; Pathology; Oncology; Cancer research; Cancer; Biology; Polymerase chain reaction; Gefitinib; Gene; Genetics","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":[0.0004230755,0.0002828748,0.0002365164,0.0004409428,0.00006737711,0.000175519,0.0001973978,0.0003373006,0.0003884451],"category_scores_gemma":[0.0005895973,0.00007534421,0.0001579205,0.0001553106,0.0001504519,0.0001100307,0.0001301386,0.0002233159,0.0001663608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002338886,"about_ca_system_score_gemma":0.0001314326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001916324,"about_ca_topic_score_gemma":0.0003800256,"domain_scores_codex":[0.9996493,0.00009845771,0.00001793442,0.00007053508,0.0001376014,0.00002620485],"domain_scores_gemma":[0.9996431,0.0001486095,0.0001030149,0.00002228804,0.0000594011,0.0000237556],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002991935,0.00005806096,0.01428989,0.0001422918,0.0000365772,0.0001933637,0.00006211331,0.001254003,0.9624557,0.0001235622,0.0003700696,0.02071518],"study_design_scores_gemma":[0.00004804767,0.00106445,0.03530541,0.00002947371,0.0001010673,0.001717477,0.00007931958,0.03268623,0.9253399,0.0003938945,0.003198778,0.00003597651],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9386891,0.004916275,0.05248,0.0003581774,0.0001084883,0.0001064983,0.0005668674,0.0005540999,0.002220413],"genre_scores_gemma":[0.9819012,0.0004598789,0.01674916,0.000116888,0.00002625107,0.00004989247,0.0001056589,0.00001397629,0.0005771095],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0004409428,"threshold_uncertainty_score":0.002237439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07922391559029132,"score_gpt":0.4022865055864462,"score_spread":0.3230625899961549,"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."}}