{"id":"W4415621606","doi":"10.1158/1078-0432.ccr-25-3143","title":"On the Road to ROME: Defining Tissue–Liquid Concordance in Precision Oncology","year":2025,"lang":"en","type":"article","venue":"Clinical Cancer Research","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"","keywords":"Concordance; Profiling (computer programming); Precision oncology; Precision medicine; Biopsy; Cancer; Liquid biopsy","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":[0.04430462,0.0007302949,0.001981526,0.001246501,0.002249106,0.006357561,0.003701292,0.01673709,0.002074263],"category_scores_gemma":[0.1250739,0.0005824327,0.001473548,0.00101802,0.01296304,0.008616969,0.004091616,0.02866665,0.001385787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004226066,"about_ca_system_score_gemma":0.007420365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004470922,"about_ca_topic_score_gemma":0.009917438,"domain_scores_codex":[0.9564168,0.02742864,0.003215872,0.003043415,0.008946237,0.0009490041],"domain_scores_gemma":[0.863171,0.1131828,0.004937559,0.002398693,0.01459939,0.001710672],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0005212312,0.00003296655,0.001569036,0.001632497,0.0002295011,0.0004233213,0.001084169,0.0004552377,0.0006728169,0.05682933,0.7984381,0.1381117],"study_design_scores_gemma":[0.0002173745,0.0003357201,0.002670313,0.006106096,0.0002736237,0.0009907638,0.001232709,0.001119311,0.001835145,0.1258782,0.8591098,0.000230985],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.0005581529,0.0761391,0.003318596,0.8871608,0.02999824,0.00003108287,0.00009881186,0.00004175801,0.002653536],"genre_scores_gemma":[0.01975001,0.03830893,0.004115257,0.833643,0.1016701,0.0001576175,0.0001132514,0.0001291893,0.002112564],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04430462,"threshold_uncertainty_score":0.2343081,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1089342623394439,"score_gpt":0.5243590187521124,"score_spread":0.4154247564126685,"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."}}