Who is the better donor for older hematopoietic transplant recipients: an older-aged sibling or a young, matched unrelated volunteer?
Bibliographic record
Abstract
Older patients are increasingly undergoing allogeneic hematopoietic transplantation. A relevant question is whether outcomes can be improved with a younger allele-level 8/8 HLA-matched unrelated donor (MUD) rather than an older HLA-matched sibling (MSD). Accordingly, transplants in leukemia/lymphoma patients age ≥50 years were analyzed comparing outcomes for recipients of MSD ≥50 (n = 1415) versus MUD <50 years (n = 757). Risks of acute graft-versus-host disease (GVHD) grade 2 to 4 (hazard ratio [HR], 1.63; P < .001), 3 to 4 (HR, 1.85; P < .001), and chronic GVHD (HR, 1.48; P < .0001) were higher after MUD compared with MSD transplants. The effect of donor type on nonrelapse mortality (NRM), relapse, and overall mortality was associated with performance score. For patients with scores of 90 or 100, NRM (HR, 1.42; P = .001), relapse (HR, 1.45; P < .001), and overall mortality (HR, 1.28; P = .001) risks were higher after MUD transplants. For patients with scores below 90, NRM (HR, 0.96; P = .76), relapse (HR, 0.86; P = .25), and overall mortality (HR, 0.90; P = .29) were not significantly different after MUD and MSD transplants. These data favor an MSD over a MUD in patients age ≥50 years.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".