Inflammation Lesions in Kidney Transplant Biopsies: Association with Survival Is Due to the Underlying Diseases
Bibliographic record
Abstract
Assessment of kidney transplant biopsies relies on nonspecific inflammatory lesions: Interstitial infiltrates (i), tubulitis (t) and intimal arteritis (v). We studied the relationship between inflammation and prognosis in biopsies for clinical indications from 314 patients (median follow-up 25 months). We used a modified Banff classification, separately assessing inflammation (i-) in nonscarred (i-Banff), scarred (i-IFTA) and whole cortex (i-total), plus tubulitis and intimal arteritis. In early biopsies (<1 year), i- and t-lesions had no association with graft survival. In late (>1 year) biopsies, all i-scores correlated with progression to failure, due to the association of these infiltrates with progressive diseases: antibody-mediated rejection (ABMR) and glomerulonephritis. Tubulitis in nonscarred areas had no impact on survival. Severe tubulitis including scarred areas (tis3) was associated with worse survival, but reflected polyoma virus nephropathy or ABMR, not T-cell-mediated rejection. Intimal arteritis (v-lesions) had no association with allograft loss in early or late biopsies. In multivariate analysis, outcome was better predicted by the presence of progressive disease than by inflammation. Thus inflammation in late kidney transplants has no inherent prognostic impact, but predicts reduced survival because inflammation indicates actively progressing diseases. The most important predictor of outcome is the diagnosis of a progressive disease.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".