Histopathological Classification and Renal Outcome in Patients with Antineutrophil Cytoplasmic Antibodies-associated Renal Vasculitis: A Study of 186 Patients and Metaanalysis
Bibliographic record
Abstract
Objective. Renal vasculitis is one of the most common manifestations of antineutrophil cytoplasmic antibodies (ANCA)-associated vasculitis (AAV) and renal histology is a key predictor of the outcome. A new histopathologic classification was proposed and validated, but the results are still debated. Methods. We performed a retrospective analysis to validate the histopathologic classification and performed a metaanalysis to evaluate its predictive value. There were 186 patients with ANCA-associated renal vasculitis diagnosed at Ruijin Hospital who were enrolled in the retrospective study. The metaanalysis considered the data for 1601 patients. Results. In our retrospective study, patients with focal class had the best renal outcome while patients with mixed class had the worst (p < 0.001). Metaanalysis showed that patients with focal class had better renal outcome than did those with crescentic class [risk ratio (RR) 0.23, 95% CI 0.16–0.34, p < 0.00001], with no evidence of heterogeneity (I2 = 0%, p = 0.96). Patients with crescentic class had better renal outcome than did those with sclerotic class (RR 0.52, 95% CI 0.41–0.64, p < 0.00001), with no evidence of heterogeneity (I2 = 2%, p = 0.43). We did not find statistical significance regarding renal outcome between mixed and crescentic classes (RR 1.14, 95% CI 0.91–1.43, p = 0.27), with no evidence of heterogeneity (I2 = 23%, p = 0.19). The retrospective study showed that lung and upper respiratory tract involvement were the most common extrarenal manifestations. Conclusion. We demonstrated the clinical utility of histopathologic classification in determining renal outcome in patients with AAV. Metaanalysis showed that patients with focal class had the best outcome while sclerotic class had the worst.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.037 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".