Evaluating Renal Disease in Pediatric‐Onset Antineutrophil Cytoplasmic Antibody–Associated Vasculitis: Disease Course, Outcomes, and Predictors of Outcome
Bibliographic record
Abstract
OBJECTIVE: We aimed to study the disease course, outcomes, and predictors of outcome in pediatric-onset antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) affecting the kidneys. METHODS: Patients eligible for this study had a diagnosis of granulomatosis with polyangiitis (GPA), microscopic polyangiitis, or ANCA-positive pauci-immune glomerulonephritis, were 18 years or younger at diagnosis, had renal disease defined by biopsy or dialysis dependence, and had clinical data at diagnosis and at either 12 or 24 months. Ambispective data from A Registry for Children with Vasculitis/Pediatric Vasculitis Initiative Registry was used. The primary outcome was inactive renal disease (pediatric vasculitis activity score = 0 or 1) at 12 months. Secondary outcomes included rates of improved renal function and damage within 24 months. Renal function, defined by estimated glomerular filtration rate, was categorized into Kidney Disease Improving Global Outcomes (KDIGO) stages at diagnosis and tested as a predictor of outcome using a proportional-odds logistic regression model. RESULTS: A total of 145 patients were included: 68% were female, and 78% had GPA. At 12 months, 83% of patients achieved inactive renal disease; however, 42% had evidence of permanent renal damage. Compared with patients with normal renal function at diagnosis, patients with moderate to severely reduced renal function, or kidney failure at diagnosis, had an odds ratio of 8.62 (P = 0.002; 95% confidence interval [CI] 2.31-32.1) and 26.3 (P < 0.001; 95% CI 6.32-109), respectively, for being in a non-normal KDIGO category at 12 months. CONCLUSION: The majority of patients with pediatric AAV achieve inactive renal disease by 12 months; however, almost half have evidence of damage. Renal function at diagnosis is a strong predictor of renal function at 12 months.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".