Relative Importance of HLA Mismatch and Donor Age to Graft Survival in Young Kidney Transplant Recipients
Bibliographic record
Abstract
BACKGROUND: The American deceased-donor (DD) kidney allocation algorithm for children emphasizes the importance of younger donors and shorter waiting times over human leukocyte antigen (HLA) matching. We sought to compare the relative importance of donor age with that of HLA mismatching (MM) on graft survival. METHODS: We studied patients less than 21 years old recorded in the U.S. Renal Data System, who received a first transplant from a DD 5 years old or younger or from a living donor (LD). Using separate Cox proportional hazards models for DD and LD recipients, we estimated the adjusted 5-year probability of graft survival for each donor age-HLA MM combination and compared estimated graft survival across the different HLA MM-donor age combinations. RESULTS: Both donor age and HLA MM were significantly associated with DD graft survival, whereas only HLA MM had a significant association with LD graft survival. Compared with DD grafts from less than 35-year-old 4-6 MM donors, survival was not significantly different for 0-1 and 2-3 MM grafts from 35- to 44-year-old donors or for 0-1 MM grafts from donors 45 years old or older. The most poorly matched grafts from the oldest LD had survival similar to or better than any DD. CONCLUSIONS: Donor age and HLA MM both play important roles in determining DD graft survival. The advantages of younger donors offset the disadvantages of poorer HLA matching, and better HLA matching offsets the disadvantages of older donor age.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".