The Risk of Failure With HLA Mismatch and Recipient Age in First Pediatric (<18 years) Kidney Transplants
Bibliographic record
Abstract
BACKGROUND: Even in the modern era of kidney transplantation with improved surgical techniques, immunosuppression, and clinical care, HLA matching has been shown to be important in allograft survival in adults who receive an organ from either a deceased or living donor. We now explore the impact of genetic matching in pediatric first-kidney transplants. METHODS: Using the United Network for Organ Sharing data, we identified 18 602 first pediatric (<18 years) kidney transplants between October 1, 1987, and December 31, 2016. Recipients were classified by number of HLA mismatches and donor origin. Cox proportional hazard analyses, adjusting for recipient and donor transplant covariates, were performed to study the impact of HLA on kidney allograft survival. RESULTS: For the fully adjusted Cox model there was a 30% increase in the hazard of allograft failure for 1 HLA mismatch, when compared with 0 mismatched recipients, and a 92% increase in risk for 6 mismatches. Although pediatric allografts from living donors survive as long or longer than those from deceased persons, they have a higher hazard of failure as a function of HLA mismatch. Kidney allografts from deceased donors HLA mismatched 0 to 3 were found to survive as long as organs from living donors HLA mismatched 4 to 6. In the full Cox model, there was a strong, linear effect on the hazard of allograft failure with quartile of age such that the youngest patients at age of transplant had the longest surviving grafts. CONCLUSIONS: HLA plays an important role in the survival of first pediatric kidney transplants. The better the match, and the earlier the transplant is performed in the child's life, the lower is the risk that the organ will fail.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".