Genomic profiles and outcomes in de novo versus therapy-related core binding factor AML
Bibliographic record
Abstract
Core binding factor acute myeloid leukemia (CBF-AML), characterized by the recurrent translocations of t(8;21)(q22;q22) or inv(16)(p13.1q22)/t(16;16)(p13.1;q22), hereafter abbreviated as t(8;21) and inv(16), is associated with favorable outcome and response well to high-dose cytarabine-based therapy [ 1 , 2 ]. Approximately 15% of CBF-AML cases are therapy-related, typically arising after topoisomerase II inhibitor exposure. While the outcomes of therapy-related acute promyelocytic leukemia are comparable to de novo cases, therapy-related CBF-AML (t-CBF-AML) has variably reported inferior outcomes compared to de novo CBF-AML (dn-CBF-AML) [ 3 , 4 , 5 , 6 ], often influenced by factors such as older age, antecedent malignancies, and cumulative treatment toxicity [ 7 ]. The ELN 2022 defined myelodysplasia-related cytogenetics (MDS-cyto) and myelodysplasia-related gene (MDS-gene) mutations are commonly found in t-AML and are associated with adverse prognosis [ 7 , 8 ], but their significance in t-CBF-AML is unclear. Secondary cytogenetic abnormalities (SCA) and gene mutations are also frequent in CBF-AML, though their prognostic significance remains controversial [ 3 , 4 , 9 , 10 , 11 ].
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".