Comparable Outcomes of Matched Sibling Donor and Matched Unrelated Donor Stem Cell Transplantation in Children With Acute Leukemia in Argentina
Bibliographic record
Abstract
BACKGROUND: Hematopoietic stem cell transplantation (HSCT) is an effective mode of consolidation therapy for children with high-risk acute leukemia. In high-income countries, match sibling donor (MSD) and match unrelated donor (MUD) HSCT have similar outcomes, but data are scarce in upper-middle-income countries. Our objective was to compare MSD and MUD HSCT outcomes for children with acute leukemia in Argentina. PATIENTS AND METHODS: This was a single-institution retrospective cohort study. We included children with acute leukemia who underwent HSCT with either MSD or MUD between 2014 and 2019. RESULTS: The study included 45 patients who received MSD (n=27) or MUD (n=18) for acute leukemia. Event-free survival was not significantly different between MSD (62.3±10.7%) versus MUD (54.2±15.0%; P=0.54) at 5 years. Similarly, there was no significant difference in 5-year overall survival between MSD (71.9±9.8%) versus MUD (65.1±13.5%; P=0.38). The cumulative incidence of treatment-related mortality (P=0.31), cumulative incidence of relapse (P=0.99), and proportion with acute-graft-versus-host disease (P=0.76) and chronic-graft-versus-host disease (P=0.68) were also not significantly different. CONCLUSIONS: In Argentina, we did not show significant differences in outcomes between MSD and MUD HSCT for children with high-risk leukemia. Future work should focus on strategies to reduce the relapse risk in children with high-risk leukemia in upper-middle-income countries.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".