{"id":"W3216722440","doi":"10.3390/cancers13225825","title":"Rational Development of Liquid Biopsy Analysis in Renal Cell Carcinoma","year":2021,"lang":"en","type":"article","venue":"Cancers","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre; McGill University; McGill Genome Centre","funders":"Servier; Fonds de Recherche du Québec - Santé; Kidney Foundation of Canada; Canadian Cancer Society Research Institute; McGill University Health Centre; Cancer Research Society; McGill University","keywords":"Liquid biopsy; Renal cell carcinoma; Somatic cell; DNA sequencing; Cancer research; Biopsy; Carcinoma; Biology; DNA; Medicine; Computational biology; Gene; Oncology; Pathology; Cancer; Internal medicine; Genetics","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":[0.001741942,0.0009310942,0.0007305156,0.001226115,0.0004562228,0.001587055,0.0008845018,0.001124858,0.001374753],"category_scores_gemma":[0.004107387,0.0005883494,0.000595581,0.0005548957,0.0006910297,0.0007775783,0.001162622,0.001093464,0.001925102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005841263,"about_ca_system_score_gemma":0.001430237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001407591,"about_ca_topic_score_gemma":0.002738913,"domain_scores_codex":[0.9978982,0.0004668398,0.0001005472,0.0004307944,0.0009851314,0.0001184668],"domain_scores_gemma":[0.9986283,0.0004800897,0.0002112925,0.0001261982,0.0004551848,0.00009898101],"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":[0.000196755,0.0000890671,0.003872396,0.0002142315,0.00003912214,0.0002182097,0.00009679692,0.002875273,0.9503632,0.001288643,0.0008492514,0.03989711],"study_design_scores_gemma":[0.00001623267,0.0003015269,0.00341183,0.00007780596,0.00004416986,0.0005897442,0.0001074235,0.0486185,0.937276,0.001401749,0.008098518,0.00005653],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1690104,0.003155242,0.8154747,0.0009363815,0.0002578175,0.0008238515,0.002005994,0.00532437,0.00301128],"genre_scores_gemma":[0.3626066,0.002320359,0.6280789,0.0008619081,0.0000869805,0.0006732171,0.002123008,0.0006340703,0.002615013],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001741942,"threshold_uncertainty_score":0.009212375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008986742211448755,"score_gpt":0.231897745762618,"score_spread":0.2229110035511692,"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."}}