The Synergistic Effect of Class II HLA Epitope-Mismatch and Nonadherence on Acute Rejection and Graft Survival
Bibliographic record
Abstract
Predicting long-term outcomes in renal transplant recipients is essential to optimize medical therapy and determine the frequency of posttransplant histologic and serologic monitoring. Nonadherence and human leukocyte antigen (HLA) mismatch are risk factors that have been associated with poor long-term outcomes and may help individualize care. In the present study, class II HLA mismatches were determined at the HLA epitope level in 195 renal transplant recipients in whom medication adherence was prospectively measured using electronic monitors in medication vial caps. Recipients were grouped by medication adherence and high (≥10 HLA-DR, ≥17 HLA-DQ) or low epitope-mismatch load. We found that the combination of higher epitope mismatch and poor adherence acted synergistically to determine the risk of rejection or graft loss. Nonadherent recipients with HLA-DR epitope mismatch ≥10 had increased graft loss (35% vs. 8%, p < 0.01) compared to adherent recipients with low epitope mismatch. At the HLA-DQ locus nonadherent recipients with HLA-DQ epitope mismatch ≥17 had increased graft loss (33% vs. 10%, p < 0.01) compared to adherent recipients with low epitope mismatch. Subclinical nonadherence early posttransplant combined with HLA class II epitope mismatch may help identify recipients that could benefit from increased clinical, histologic, and serologic monitoring.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".