Identification of a permissible HLA mismatch in hematopoietic stem cell transplantation
Bibliographic record
Abstract
In subjects mismatched in the HLA alleles C*03:03/C*03:04 no allogeneic cytotoxic T-lymphocyte responses are detected in vitro. Hematopoietic stem cell transplantation (HSCT) with unrelated donors (UDs) showed no association between the HLA-C allele mismatches (CAMMs) and adverse outcomes; antigen mismatches at this and mismatches other HLA loci are deleterious. The absence of effect of the CAMM may have resulted from the predominance of the mismatch C*03:03/C*03:04. Patients with hematologic malignancies receiving UD HSCT matched in 8/8 and 7/8 HLA alleles were examined. Transplants mismatched in HLA-C antigens or mismatched in HLA-A, -B, or -DRB1 presented significant differences (P < .0001) in mortality (hazard ratio [HR] = 1.37, 1.30), disease-free survival (HR = 1.33, 1.27), treatment-related mortality (HR = 1.54, 1.54), and grade 3-4 acute graft-versus-host disease (HR = 1.49, 1.77) compared with the 8/8 group; transplants mismatched in other CAMMs had similar outcomes with HR ranging from 1.34 to 172 for these endpoints. The C*03:03/C*03:04 mismatched and the 8/8 matched groups had identical outcomes (HR ranging from 0.96-1.05). The previous finding that CAMMs do not associate with adverse outcomes is explained by the predominance (69%) of the mismatch C*03:03/03:04 in this group that is better tolerated than other HLA mismatches.
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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 | 0.003 |
| 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".