HLA mismatching within or outside of cross-reactive groups (CREGs) is associated with similar outcomes after unrelated hematopoietic stem cell transplantation
Bibliographic record
Abstract
The National Marrow Donor Program maintains a registry of volunteer donors for patients in need of a hematopoietic stem cell transplantation. Strategies for selecting a partially HLA-mismatched donor vary when a full match cannot be identified. Some transplantation centers limit the selection of mismatched donors to those sharing mismatched antigens within HLA-A and HLA-B cross-reactive groups (CREGs). To assess whether an HLA mismatch within a CREG group ("minor") may result in better outcome than a mismatch outside CREG groups ("major"), we analyzed validated outcomes data from 2709 bone marrow and peripheral blood stem cell transplantations. Three-hundred and ninety-six pairs (15%) were HLA-DRB1 allele matched but had an antigen-level mismatch at HLA-A or HLA-B. Univariate and multivariate analyses of engraftment, graft-versus-host disease, and survival showed that outcome is not significantly different between minor and major mismatches (P = .47, from the log-rank test for Kaplan-Meier survival). However, HLA-A, HLA-B, and HLA-DRB1 allele-matched cases had significantly better outcome than mismatched cases (P < .001). For patients without an HLA match, the selection of a CREG-compatible donor as tested does not improve outcome.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".