{"id":"W3168034500","doi":"10.1186/s13046-021-01984-w","title":"Circulating tumor DNA tracking through driver mutations as a liquid biopsy-based biomarker for uveal melanoma","year":2021,"lang":"en","type":"article","venue":"Journal of Experimental & Clinical Cancer Research","topic":"Ocular Oncology and Treatments","field":"Medicine","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier de l’Université de Montréal; McGill University; McGill University Health Centre","funders":"Consejo Nacional de Ciencia y Tecnología, Guatemala; Mitacs; Consejo Nacional de Ciencia y Tecnología; McGill University Health Centre","keywords":"Liquid biopsy; Digital polymerase chain reaction; Melanoma; Biopsy; Medicine; Biomarker; Circulating tumor cell; Cell-free fetal DNA; Cancer; Pathology; Primary tumor; Cancer research; Metastasis; Oncology; Internal medicine; Gene; Biology; Polymerase chain reaction","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007493636,0.0003515745,0.0002677035,0.0007476777,0.0001456488,0.0005299203,0.0002740619,0.0005193645,0.0004956401],"category_scores_gemma":[0.0008250028,0.0001833508,0.0001793897,0.0002793385,0.0003742338,0.000303015,0.0002679553,0.0003746006,0.00022114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003449515,"about_ca_system_score_gemma":0.0002066198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005132251,"about_ca_topic_score_gemma":0.000859332,"domain_scores_codex":[0.9994592,0.0001536766,0.00003101156,0.0001455443,0.0001760789,0.0000345067],"domain_scores_gemma":[0.9995945,0.0001338261,0.0001166087,0.0000305701,0.00008737927,0.00003709205],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0003364606,0.0000785185,0.03063911,0.0001646365,0.00002350122,0.0001635428,0.00009679158,0.0004333309,0.9526844,0.00008960212,0.0001036259,0.01518643],"study_design_scores_gemma":[0.00003302015,0.001446979,0.03562864,0.00005250332,0.0001164214,0.002581276,0.0001432285,0.0158199,0.9413602,0.000247348,0.002542384,0.00002820299],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9507998,0.007299749,0.04006597,0.0001699913,0.00006781839,0.0001460709,0.0002524042,0.0002250718,0.0009731708],"genre_scores_gemma":[0.9671677,0.001287594,0.03048856,0.0001439733,0.00002373385,0.00006344243,0.0001883175,0.00001736611,0.0006193991],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007493636,"threshold_uncertainty_score":0.003963053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2838010330003053,"score_gpt":0.5757009819376683,"score_spread":0.291899948937363,"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."}}