{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002592313,0.0001932931,0.0004986672,0.00005541054,0.00006243878,0.00001386639,0.0002325109,0.0003813724,0.000001265624],"category_scores_gemma":[0.0008303825,0.0001299922,0.0001395806,0.00007918278,0.0002615006,0.000001620272,0.0001586016,0.0001878458,2.839102e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005367412,"about_ca_system_score_gemma":0.0001105261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001738533,"about_ca_topic_score_gemma":0.0001056095,"domain_scores_codex":[0.9984323,0.0002588637,0.0005105752,0.000415609,0.00009679967,0.0002858749],"domain_scores_gemma":[0.99867,0.0007802243,0.0001592116,0.0002627175,0.00007154668,0.00005633767],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000829818,0.001713728,0.9027605,0.0002617498,0.0001568748,0.00005239018,0.00005821198,0.0001307128,0.07641452,0.01171921,0.0007339998,0.005168309],"study_design_scores_gemma":[0.002172275,0.001966507,0.9713116,0.00008188254,0.0001717473,0.00006503663,0.0001549084,0.0004036087,0.01571503,0.007291525,0.0004641703,0.0002017289],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.986141,0.007854634,0.003990055,0.0007936735,0.0003593703,0.0007298961,0.00002583847,0.000007510359,0.00009800696],"genre_scores_gemma":[0.9969217,0.001299378,0.001203712,0.0002285819,0.0000721117,0.0002020462,0.00003849571,0.00001014551,0.0000238867],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0685511,"threshold_uncertainty_score":0.5300927,"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."}}