{"id":"W4411356627","doi":"10.1002/hon.70094_422","title":"422 | MOLECULAR FEATURES ENCODED IN THE DYNAMIC ctDNA MONITORING REVEAL PROGNOSTIC VALUE, DIFFERENT CLINICAL COURSES AND CLONE EVOLUTION FOR DIFFERENT GENETIC SUBTYPES OF DLBCL","year":2025,"lang":"en","type":"article","venue":"Hematological Oncology","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; Journal of Gastroenterology and Hepatology Foundation; Incyte; Québec Consortium for Drug Discovery; Gilead Sciences","keywords":"clone (Java method); Value (mathematics); Somatic evolution in cancer; Oncology; Computational biology; Biology; Internal medicine; Medicine; Genetics; Computer science; Gene; Cancer; Machine learning","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003317065,0.0001811503,0.0002302462,0.0006295702,0.00015579,0.0005820112,0.0001898524,0.0002618543,0.001679451],"category_scores_gemma":[0.0007594816,0.0001098324,0.0001168927,0.000516318,0.0002273024,0.0002879567,0.00019691,0.0002514166,0.0003690935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002378724,"about_ca_system_score_gemma":0.0001265861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004526342,"about_ca_topic_score_gemma":0.0005144142,"domain_scores_codex":[0.9998087,0.00003281225,0.00002374276,0.00005992244,0.00004495381,0.00002990477],"domain_scores_gemma":[0.9995486,0.00008710737,0.000233428,0.00002924004,0.00003766675,0.00006390159],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006289989,0.00003080534,0.9728038,0.0000161594,0.00002246186,0.0001666772,0.00005050835,0.0001302498,0.01752592,0.00002857366,0.00009976287,0.008495981],"study_design_scores_gemma":[0.0000122341,0.0002397686,0.991977,0.0000058358,0.00002614821,0.001813081,0.00006384029,0.0006777907,0.004479148,0.00007980919,0.0006214981,0.00000378837],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986526,0.0002396915,0.0002770632,0.00002011043,0.000002146302,0.000006111009,0.0003408042,0.000008032524,0.000453455],"genre_scores_gemma":[0.9990118,0.00006627314,0.0002301039,0.00001591305,0.00000620513,0.000006712265,0.0004444739,0.000002810529,0.0002158285],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001679451,"threshold_uncertainty_score":0.005618334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01547472092305363,"score_gpt":0.3414114349444002,"score_spread":0.3259367140213466,"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."}}