Re-Examining Risk of Repeated HLA Mismatch in Kidney Transplantation
Bibliographic record
Abstract
Kidney retransplantation is a risk factor for decreased allograft survival. Repeated mismatched HLA antigens between first and second transplant may be a stimulus for immune memory responses and increased risk of alloimmune damage to the second allograft. Historical data identified a role of repeated HLA mismatches in allograft loss. However, evolution of HLA testing methods and a modern transplant era necessitate re-examination of this role to more accurately risk-stratify recipients. We conducted a contemporary registry analysis of data from 13,789 patients who received a second kidney transplant from 1995 to 2011, of which 3868 had one or more repeated mismatches. Multivariable Cox proportional hazards modeling revealed no effect of repeated mismatches on all-cause or death-censored graft loss. Analysis of predefined subgroups, however, showed that any class 2 repeated mismatch increased the hazard of death-censored graft loss, particularly in patients with detectable panel-reactive antibody before second transplant (hazard ratio [HR], 1.15; 95% confidence interval [95% CI], 1.02 to 1.29). Furthermore, in those who had nephrectomy of the first allograft, class 2 repeated mismatches specifically associated with all-cause (HR, 1.30; 95% CI, 1.07 to 1.58) and death-censored graft loss (HR, 1.41; 95% CI, 1.12 to 1.78). These updated data redefine the effect of repeated mismatches in retransplantation and challenge the paradigm that repeated mismatches in isolation confer increased immunologic risk. We also defined clear recipient categories for which repeated mismatches may be of greater concern in a contemporary cohort. Additional studies are needed to determine appropriate interventions for these recipients.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".