Decreased Infections in Recipients of Unrelated Donor Hematopoietic Cell Transplantation from Donors with an Activating KIR Genotype
Bibliographic record
Abstract
Infectious complications following allogeneic hematopoietic cell transplantation (HCT) from unrelated donors (URD) result in significant morbidity. We hypothesized that recipients of a URD with an activating natural killer cell immunoglobulin-like receptor (KIR) (B/x) genotype would have decreased infectious complications because of enhanced natural killer (NK) cell function. We compared the infectious complications in 116 recipients of a graft from a donor with an A/A KIR (n = 44) genotype and a B/x KIR (n = 72) genotype. All recipients participated in the prospective National Marrow Donor Program infection project collecting infection data from conditioning until 6 months posttransplant. The cohort with a B/x donor had fewer initial bacterial infections by day 180 (A/A: 86%; 95% confidence interval [CI], 75-95; B/x: 68%; 95% CI, 57-78; P = .02). There was no difference in the incidence of viral or fungal infections. When accounting for multiple infections, fewer bacterial infections were seen in the B/x cohort (A/A: 3.55/patient; B/x: 2.63/patient; P = .09). During the study period, only 19 patients had no infections; of these, 15 had received cells from a B/x KIR donor. The role of donor KIR genotype on infection complications is intriguing and warrants further investigation.
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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 | 0.001 | 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".