Impact of Donor and Recipient Sex and Parity on Outcomes of HLA-Identical Sibling Allogeneic Hematopoietic Stem Cell Transplantation
Bibliographic record
Abstract
Allogeneic hematopoietic stem cell transplantation (SCT) may cure patients with hematologic malignancies, but it carries significant risks. Careful donor selection is an important component of the clinical transplantation decision-making process and includes evaluation of HLA typing and other criteria, the most controversial of which is parity. We examined the effect of donor sex and parity on outcomes of HLA-identical sibling SCT. Because the effect of recipient sex/parity has never been explicitly evaluated, we also analyzed the effect of recipient sex/parity on outcomes of transplantation. We found that (1) parous female donors result in an increased risk of chronic graft-versus-host disease (GVHD) in all recipients, (2) the magnitude of this increased risk is similar in male and female recipients, and (3) nulliparous female donors increase the risk of chronic GVHD in male recipients to a degree comparable to that from parous donors. A decrease in the risk of relapse was not observed, and there was no effect on overall survival, acute GVHD, or transplant-related mortality. Recipient parity had no independent effect on any endpoint. Until the effects of pregnancy on the maternal immune system are better understood, it is appropriate whenever possible to avoid parous female donors and to choose male donors for male recipients in HLA-identical related donor 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".