The Effect of KIR Ligand Incompatibility on the Outcome of Unrelated Donor Transplantation: A Report from the Center for International Blood and Marrow Transplant Research, the European Blood and Marrow Transplant Registry, and the Dutch Registry
Bibliographic record
Abstract
Matching for HLA class I alleles, including HLA-C, is an important criterion for outcome of unrelated donor transplantation. However, haplotype-mismatched transplantations for myeloid malignancies, mismatched for killer immunoglobulin-like receptor (KIR) ligands in the graft-versus-host (GVH) direction, is associated with lower rates of graft-versus-host disease (GVHD), relapse, and mortality. This study investigated the effect of KIR ligand mismatching on the outcome of unrelated donor transplantation. The outcomes after 1571 unrelated donor transplantations for myeloid malignancies where donor-recipient pairs were HLA-A, -B, -C, and -DRB1 matched (n = 1004), GVH KIR ligand-mismatched (n = 137), host-versus-graft (HVG) KIR ligand-mismatched (n = 170), and HLA-B and/or -C-mismatched but KIR ligand-matched (n = 260) were compared using Cox regression models. Treatment-related mortality (TRM), treatment failure, and overall mortality were lowest after matched transplantations. Patients who received grafts from donors mismatched at the KIR ligand in the GVH or HVG direction and mismatched at HLA-B and/or C but matched at the KIR ligand had similar rates of TRM, treatment failure, and overall mortality. There were no differences in leukemia recurrence between the 4 groups. These results do not support the choice of an unrelated donor on the basis of KIR ligand mismatch determined from HLA typing.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".