Cytarabine dose reduction in patients with low‐risk acute myeloid leukemia: A report from the Children's Oncology Group
Bibliographic record
Abstract
BACKGROUND: The optimal number of chemotherapy courses for low-risk (LR) pediatric acute myeloid leukemia (AML) is not known. OBJECTIVE: cytarabine) chemotherapy courses for LR-AML using data from Children's Oncology Group (COG) AAML0531 and AAML1031. METHODS: We compared relapse risk (RR), disease-free survival (DFS), and overall survival (OS), and the differential impact in LR subgroups for patients receiving four versus five chemotherapy courses. Cox (OS and DFS) and risk (RR) regressions were used to estimate hazard ratios (HR) to compare outcomes. RESULTS: A total of 923 LR-AML patients were included; 21% received five courses. Overall, LR-AML patients who received four courses had higher RR (40.9% vs. 31.4%; HR = 1.40, 95% confidence interval [CI]: 1.06-1.85), and worse DFS (56.0% vs. 67.0%; HR = 1.45, 95% CI: 1.10-1.91). There was a similar decrement in OS though it was not statistically significant (77.0% vs. 83.5%; HR = 1.45, 95% CI: 0.97-2.17). Stratified analyses revealed the detrimental effects of cytarabine dose de-escalation to be most pronounced in the LR-AML subgroup with uninformative cytogenetic/molecular features who were minimal residual disease (MRD) negative after the first induction course (EOI1). The absolute decrease in DFS with four courses for patients with favorable cytogenetic/molecular features and positive MRD was similar to that observed for patients with uninformative cytogenetic/molecular features and negative MRD at EOI1, though not statistically significant. CONCLUSIONS: Our results support de-escalation of cytarabine exposure through the elimination of a fifth chemotherapy course only for LR-AML patients who have both favorable cytogenetic/molecular features and negative MRD after the first induction cycle.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".