Decreased induction morbidity and mortality following modification to induction therapy in infants with acute lymphoblastic leukemia enrolled on AALL0631: A report from the children's oncology group
Bibliographic record
Abstract
BACKGROUND: Infants with acute lymphoblastic leukemia (ALL) have a poor prognosis. Intensification of therapy has resulted in fewer relapses but increased early deaths, resulting in failure to improve survival. PROCEDURE: AALL0631 is a Phase 3 study for infants (<366 days of age) with newly diagnosed ALL. Induction initially (Cohort 1) consisted of 3 weeks of therapy based on COG P9407. Due to excessive early mortality, induction was amended to a less intensive 5 weeks of therapy based on Interfant-99. Additionally, enhanced supportive care guidelines were incorporated with hospitalization during induction until evidence of marrow recovery and recommendations for prevention/treatment of infections (Cohort 2). RESULTS: Induction mortality was significantly lower for patients in Cohort 2 (2/123, 1.6%) versus Cohort 1 (4/26, 15.4%; P = 0.009). All induction deaths were infection related except one due to progressive disease (Cohort 2). Sterile site infections were lower for patients in Cohort 2 (24/123, 19.5%) versus Cohort 1 (15/26, 57.7%; P = 0.0002), with a significantly lower rate of Gram positive infections during induction for patients in Cohort 2, P = 0.0002. No clinically significant differences in grades 3-5 non-infectious toxicities were observed between the two cohorts. Higher complete response rates were observed at end induction intensification for Cohort 2 (week 9, 94/100, 94%) versus Cohort 1 (week 7, 17/25, 68%; P = 0.0.0012). CONCLUSION: De-intensification of induction therapy and enhanced supportive care guidelines significantly decreased induction mortality and sterile site infections, without decreasing complete remission rates.
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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 | 0.002 | 0.004 |
| 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) | 0.000 | 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".