Peritubular Capillaritis in Renal Allografts: Prevalence, Scoring System, Reproducibility and Clinicopathological Correlates
Bibliographic record
Abstract
While glomerulitis is graded according to the Banff classification, no criteria for scoring peritubular capillaritis (PTC) have been established. We retrospectively applied PTC-scoring criteria to 688 renal allograft (46 preimplantation, 461 protocol, 181 indication) biopsies. A total of 26.3% of all analyzed biopsies had peritubular capillaritis (implant 0%, protocol 17.6%, indication 45.5%; p < 0.0001). The most common capillaritis pattern was of moderate severity (5-10 luminal cells), focal in extent (10-50% of PTC), with a minority of neutrophils. A total of 24% of C4d- compared with 75% of C4d+ biopsies showed capillaritis (p < 0.0001). More than 80% of biopsies with glomerulitis had peritubular capillaritis. A total of 50.4% of biopsies with borderline or T-cell mediated rejection (TCMR) and 14.1% of biopsies without TCMR or antibody-mediated rejection (ABMR) showed capillaritis (p < 0.0001). The inter-observer reproducibility of the PTC-scoring features was fair to moderate. Diffuse capillaritis detected in early protocol biopsies had significant negative prognostic impact in terms of glomerular filtration rate 2 years posttransplantation. Indication biopsies show a significantly higher prevalence of capillaritis than protocol biopsies (45.5% vs. 17.6%; p < 0.0001). Capillaritis is more frequent and pronounced in ABMR, but can be observed in TCMR cases. Thus, scoring of peritubular capillaritis is feasible and can provide prognostic and diagnostic information in renal allograft biopsies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".