Evidence for the alloimmune basis and prognostic significance of Borderline T cell–mediated rejection
Bibliographic record
Abstract
Prognostic biomarkers of T cell-mediated rejection (TCMR) have not been adequately studied in the modern era. We evaluated 803 renal transplant recipients and correlated HLA-DR/DQ molecular mismatch alloimmune risk categories (low, intermediate, high) with the severity, frequency, and persistence of TCMR. Allograft survival was reduced in recipients with Banff Borderline (hazard ratio [HR] 2.4, P = .003) and Banff ≥ IA TCMR (HR 4.3, P < .0001) including a subset who never developed de novo donor-specific antibodies (P = .002). HLA-DR/DQ molecular mismatch alloimmune risk categories were multivariate correlates of Banff Borderline and Banff ≥ IA TCMR and correlated with the severity and frequency of rejection episodes. Recipient age, HLA-DR/DQ molecular mismatch category, and cyclosporin vs tacrolimus immunosuppression were independent correlates of Banff Borderline and Banff ≥ IA TCMR. In the subset treated with tacrolimus (720/803) recipient age, HLA-DR/DQ molecular mismatch category, and tacrolimus coefficient of variation were independent correlates of TCMR. The correlation of HLA-DR/DQ molecular mismatch category with TCMR, including Borderline, provides evidence for their alloimmune basis. HLA-DR/DQ molecular mismatch may represent a precise prognostic biomarker that can be applied to tailor immunosuppression or design clinical trials based on individual patient risk.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".