Birth Order and Transplantation Outcome in HLA-Identical Sibling Stem Cell Transplantation: An Analysis on Behalf of the Center for International Blood and Marrow Transplantation
Bibliographic record
Abstract
Allogeneic stem cell transplantation (SCT) is the most effective treatment option for many hematologic malignancies, but graft-versus-host disease (GVHD) remains a major cause of treatment failure. Along with well-established risk factors for transplantation outcomes, recent single-center studies have identified a birth order effect in HLA-identical sibling SCT, with lower rates of acute and chronic GVHD and improved overall survival when the donor is younger than the recipient. One hypothesized mechanism for this effect is microchimerism due to fetomaternal and transmaternal sibling cell trafficking during pregnancy as the donor is exposed to recipient antigens in utero. The aim of the present study was to validate previously reported single-center data in a large, multicenter cohort provided by the Center for International Blood and Marrow Transplantation. All adult and pediatric patients (n = 11,365) with a hematologic malignancy who underwent allogeneic SCT with a graft from an HLA-identical sibling donor between 1990 and 2007 were included. When donors were younger than recipients, there was a significantly lower rate of acute GVHD grade II-IV and chronic GVHD in children, as well as a lower rate of chronic GVHD in adolescents. However, the hypothesized overall positive effect of lower relapse and better survival when donors are younger than recipients was not observed. Our data suggest that if otherwise equally matched, a graft from a younger sibling may be superior to a graft from an older sibling for children and adolescents undergoing SCT.
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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 | 0.004 |
| 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 | 0.001 |
| 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".