Nonpermissive HLA-DPB1 mismatch increases mortality after myeloablative unrelated allogeneic hematopoietic cell transplantation
Bibliographic record
Abstract
We examined current outcomes of unrelated donor allogeneic hematopoietic cell transplantation (HCT) to determine the clinical implications of donor-recipient HLA matching. Adult and pediatric patients who had first undergone myeloablative-unrelated bone marrow or peripheral blood HCT for acute myelogenous leukemia, acute lymphoblastic leukemia, chronic myelogenous leukemia, and myelodysplastic syndrome between 1999 and 2011 were included. All had high-resolution typing for HLA-A, -B, -C, and -DRB1. Of the total (n = 8003), cases were 8/8 (n = 5449), 7/8 (n = 2071), or 6/8 (n = 483) matched. HLA mismatch (6-7/8) conferred significantly increased risk for grades II to IV and III to IV acute graft vs host disease (GVHD), chronic GVHD, transplant-related mortality (TRM), and overall mortality compared with HLA-matched cases (8/8). Type (allele/antigen) and locus (HLA-A, -B, -C, and -DRB1) of mismatch were not associated with overall mortality. Among 8/8 matched cases, HLA-DPB1 and -DQB1 mismatch resulted in increased acute GVHD, and HLA-DPB1 mismatch had decreased relapse. Nonpermissive HLA-DPB1 allele mismatch was associated with higher TRM compared with permissive HLA-DPB1 mismatch or HLA-DPB1 match and increased overall mortality compared with permissive HLA-DPB1 mismatch in 8/8 (and 10/10) matched cases. Full matching at HLA-A, -B, -C, and -DRB1 is required for optimal unrelated donor HCT survival, and avoidance of nonpermissive HLA-DPB1 mismatches in otherwise HLA-matched pairs is indicated.
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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 | 0.000 | 0.002 |
| 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".