Outcomes of intensification of induction chemotherapy for children with high‐risk acute myeloid leukemia: A report from the Children's Oncology Group
Bibliographic record
Abstract
BACKGROUND: High-risk pediatric acute myeloid leukemia confers a poor prognosis, and alternative strategies are needed to improve outcomes. We hypothesized that intensifying induction on the AAML1031 clinical trial would improve outcomes compared to the predecessor trial AAML0531. METHODS: ) (MA). Stem cell transplant (SCT) conditioning included busulfan/cyclophosphamide on AAML0531, whereas AAML1031 used busulfan/fludarabine and liberalized donor eligibility. Patients were included in this analysis if they met high-risk criteria common to the two trials by cytogenics or poor disease response after induction I ADE. RESULTS: MA provided no benefit over ADE at: induction II response (complete response [CR]: 64% vs. 62%, p = .87; measurable residual disease [MRD]+: 57% vs. 46%, p = .34); or intensification I response (CR: 79% vs. 94%, p = .27; MRD+: 27% vs. 20%, p = 1.0). When considered with altered SCT approach, MA did not improve 5-year disease-free survival (24% ± 9% vs. 18% ± 15%, p = .63) or 5-year overall survival (35% ± 10% vs. 38% ± 18%, p = .66). MA was associated with slower neutrophil recovery (median 34 vs. 27 days, p = .007) and platelet recovery (median 29 vs. 24.5 days, p = .04) and longer hospital stay (32 vs. 28 days, p = .01) during induction II. CONCLUSION: Intensification of induction II did not improve treatment response or survival, but did increase toxicity and resource utilization. Alternative strategies are urgently needed to improve outcomes for pediatric patients with high-risk acute myeloid leukemia (trials registered at clinicaltrials.gov NCT01371981, NCT00372593).
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".