Cluster Analysis of Lesions in Nonselected Kidney Transplant Biopsies: Microcirculation Changes, Tubulointerstitial Inflammation and Scarring
Bibliographic record
Abstract
Banff classification empirically established scoring of histologic lesions, but the relationships of lesions to each other and to underlying biologic processes remain unclear. We hypothesized that class discovery tools would reveal new relationships between individual lesions, and relate lesions to C4d staining, anti-HLA donor-specific antibody (DSA) and time posttransplant. We studied 234 nonselected renal allograft biopsies for clinical indications from 173 patients. Silhouette plotting and principal component analysis revealed three groups of lesions: microcirculation changes, including inflammation (glomerulitis, capillaritis) and deterioration (double contours, mesangial expansion); scarring/hyalinosis; and tubulointerstitial inflammation. DSA and C4d grouped with microcirculation inflammation, whereas time posttransplant grouped with scarring/hyalinosis lesions. Intimal arteritis clustered with DSA, C4d and microcirculation inflammation, but also showed correlations with tubulitis. Fibrous intimal thickening in arteries clustered with scarring/hyalinosis. Capillary basement membrane multilayering showed intermediary relationships between microcirculation deterioration and time-dependent scarring. Correlation analysis and hierarchical clustering confirmed the lesion relationships. Thus, we propose that the pathologic lesions in biopsies are not independent but are members of groups that represent distinct pathogenic forces: microcirculation changes, reflecting the stress of DSA; scarring, hyalinosis and arterial fibrosis, reflecting the cumulative burden of injury over time; and tubulointerstitial inflammation. Interpretation of lesions should reflect these associations.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".