Referee report. For: ANCA-associated vasculitis in Ireland: a multi-centre national cohort study [version 1; peer review: 2 approved]
Bibliographic record
Abstract
Background: Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a rare multisystem autoimmune disease. There is a need for interoperable national registries to enable reporting of real-world long-term outcomes and their predictors in AAV. Methods: The Irish National Rare Kidney Disease (RKD) registry was founded in 2012. To date, 842 patients with various forms of vasculitis have been recruited across eight nephrology, rheumatology and immunology centres. We focus here on patient- and disease- characteristics, treatment and outcomes of the 397 prospectively recruited patients with AAV. Results: Median age was 64 years (IQR 55–73), 57.9% were male, 58.9% had microscopic polyangiitis and 85.9% had renal impairment. Cumulative one- and five-year patient survival was 94% and 77% respectively. Median follow-up was 33.5 months (IQR 10.7–52.7). After controlling for age, baseline renal dysfunction (p = 0.04) and the burden of adverse events (p <0.001) were independent predictors of death overall. End-stage-kidney-disease (ESKD) occurred in 73 (18.4%) patients; one- and five-year renal survival was 85% and 79% respectively. Baseline severity of renal insufficiency (p = 0.02), urine soluble CD163 (usCD163) (p = 0.002) and “sclerotic” Berden histological class (p = 0.001) were key determinants of ESKD risk. Conclusions: Long-term outcomes of Irish AAV patients are comparable to other reported series. Our results emphasise the need for personalisation of immunosuppression, to limit treatment toxicity, particularly in those with advanced age and renal insufficiency. Baseline usCD163 is a potential biomarker for ESKD prediction and should be validated in a large independent cohort.
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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 | 0.012 | 0.277 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.386 | 0.123 |
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".