Impact of HLA Mismatch at First Kidney Transplant on Lifetime With Graft Function in Young Recipients
Bibliographic record
Abstract
As HLA matching has been progressively de-emphasized in the American deceased donor (DD) kidney allocation algorithm, concerns have been raised that poor matching at first transplant may lead to greater sensitization and more difficulty finding an acceptable donor for a second transplant should the first transplant fail. We compared proportion of total observed lifetime with graft function after first transplant, and waiting times for a second transplant between individuals with different levels of HLA mismatch (MM) at first transplant. We studied patients recorded in the United States Renal Data System (1988-2009) who received a first DD transplant at age ≤21 years (n = 8433), and the subgroup who were listed for a second DD transplant following first graft failure (n = 2498). Compared with recipients of 2-3 MM first grafts, 4-6 MM graft recipients spent 12% less of their time and 0-1 MM recipients 15% more time with a functioning graft after the first transplant (both p < 0.0001); 4-6 MM recipients were significantly less likely (hazard ratio [HR] 0.87 [95% confidence interval 0.76, 0.98]; p = 0.03), and 0-1 MM recipients more likely (HR 1.26 [0.99, 1.60]; p = 0.06) to receive a second transplant after listing. The benefits of better HLA matching at first transplant on lifetime with graft function are significant, but relatively small.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".