The negative impact of T cell–mediated rejection on renal allograft survival in the modern era
Bibliographic record
Abstract
The prevalence and long-term impact of T cell-mediated rejection (TCMR) is poorly defined in the modern era of tacrolimus/mycophenolate-based maintenance therapy. This observational study evaluated 775 kidney transplant recipients with serial histology and correlated TCMR events with the risk of graft loss. After a ~30% incidence of a first Banff Borderline or greater TCMR detected on for-cause (17%) or surveillance (13%) biopsies, persistent (37.4%) or subsequent (26.3%) TCMR occurred in 64% of recipients on follow-up biopsies. Alloimmune risk categories based on the HLA-DR/DQ single molecule eplet molecular mismatch correlated with the number of TCMR events (p = .002) and Banff TCMR grade (p = .007). Both a first and second TCMR event correlated with death-censored and all-cause graft loss when adjusted for baseline covariates and other significant time-dependent covariates such as DGF and ABMR. Therefore, a substantial portion of kidney transplant recipients, especially those with intermediate and high HLA-DR/DQ molecular mismatch scores, remain under-immunosuppressed, which in turn identifies the need for novel agents that can more effectively prevent or treat TCMR.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".