Tubular Lesions Predict Renal Outcome in Antineutrophil Cytoplasmic Antibody–Associated Glomerulonephritis after Rituximab Therapy
Bibliographic record
Abstract
Histopathological features in renal biopsies of patients with antineutrophil cytoplasmic antibody-associated vasculitis have predictive value for renal outcome in patients who receive standard treatment with cyclophosphamide and corticosteroids; however, whether the same holds true for rituximab-treated patients is unknown. We describe associations between renal histopathology and outcomes among patients treated with a rituximab-based regimen in the Randomized Trial of Rituximab versus Cyclophosphamide in ANCA-Associated Vasculitis trial. Two pathologists, blinded to clinical data, reviewed biopsies from 30 patients according to a standardized protocol that included assessment of T cell, B cell, and plasma cell infiltration, as well as scoring for tubulitis, interstitial inflammation, and glomerulitis. We did not observe associations between immunohistology scores and age, sex, estimated GFR at entry, or requirement for dialysis. However, tubulointerstitial inflammation was more severe among patients who had a positive test for the myeloperoxidase antineutrophil cytoplasmic antibody. In a multiple linear regression model, both CD3(+) T cell tubulitis and tubular atrophy independently associated with estimated GFR at 12 months. Tubular atrophy remained an independent predictor at 24 months (P<0.01). These results suggest that in addition to anti-B cell therapy, therapy directed at T cells may improve renal outcomes in antineutrophil cytoplasmic antibody-associated vasculitis.
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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 | 0.000 |
| 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".