Inflammation in Areas of Tubular Atrophy in Kidney Allograft Biopsies: A Potent Predictor of Allograft Failure
Bibliographic record
Abstract
The Banff scoring schema provides a common ground to analyze kidney transplant biopsies. Interstitial inflammation (i) and tubulitis (t) in areas of viable tissue are features in scoring acute rejection, but are excluded in areas of tubular atrophy (TA). We studied inflammation and tubulitis in a cohort of kidney transplant recipients undergoing allograft biopsy for new-onset late graft dysfunction (N = 337). We found inflammation ('iatr') and tubulitis ('tatr') in regions of fibrosis and atrophy to be strongly correlated with each other (p < 0.0001). Moreover, iatr was strongly associated with death-censored graft failure when compared to recipients whose biopsies had no inflammation, even after adjusting for the presence of interstitial fibrosis (Hazard Ratio = 2.31, [1.10-4.83]; p = 0.0262) or TA (hazard ratio = 2.42, [1.16-5.08]; p = 0.191), serum creatinine at the time of biopsy, time to biopsy and i score. Further, these results did not qualitatively change after additional adjustments for C4d staining or donor specific antibody. Stepwise regression identified the most significant markers of graft failure which include iatr score. We propose that a more global assessment of inflammation in kidney allograft biopsies to include inflammation in atrophic areas may provide better prognostic information. Phenotypic characterization of these inflammatory cells and appropriate treatment may ameliorate late allograft failure.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".