High-dose AraC is essential for the treatment of ML-DS independent of postinduction MRD: results of the COG AAML1531 trial
Bibliographic record
Abstract
Myeloid leukemia in children with Down syndrome (ML-DS) is associated with young age and somatic GATA1 mutations. Because of high event-free survival (EFS) and hypersensitivity of the leukemic blasts to chemotherapy, the prior Children's Oncology Group protocol ML-DS protocol (AAML0431) reduced overall treatment intensity but lacking risk stratification, retained the high-dose cytarabine course (HD-AraC), which was highly associated with infectious morbidity. Despite high EFS of ML-DS, survival for those who relapse is rare. AAML1531 introduced therapeutic risk stratification based on the previously identified prognostic factor, measurable residual disease (MRD) at the end of the first induction course. Standard risk (SR) patients were identified by negative MRD using flow cytometry (<0.05%) and did not receive the historically administered HD-AraC course. Interim analysis of 114 SR patients revealed a 2-year EFS of 85.6% (95% confidence interval [CI], 75.7-95.5), which was significantly lower than for MRD- patients treated with HD-AraC on AAML0431 (P = .0002). Overall survival at 2 years was 91.0% (95% CI, 83.8-95.0). Twelve SR patients relapsed, mostly within 1 year from study entry and had a 1-year OS of 16.7% (95% CI, 2.7-41.3). Complex karyotypes were more frequent in SR patients who relapsed compared with those who did not (36% vs 9%; P = .0248). MRD by error-corrected sequencing of GATA1 mutations was piloted in 18 SR patients and detectable in 60% who relapsed vs 23% who did not (P = .2682). Patients with SR ML-DS had worse outcomes without HD-AraC after risk classification based on flow cytometric MRD.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".