Renal relapse in antineutrophil cytoplasmic autoantibody-associated vasculitis: unpredictable, but predictive of renal outcome
Bibliographic record
Abstract
Objectives: To determine predictors of renal relapse and end-stage renal failure (ESRF) in patients with ANCA-associated vasculitis. Methods: Data from four European Vasculitis Society randomized controlled trials, conducted roughly simultaneously between 15 March 1995 and 30 September 2002, was pooled to determine predictors of long-term renal outcome. The respective trial inclusion criteria covered the entire spectrum of disease severity. Baseline predictors of time to first renal relapse and time to ESRF were assessed by competing events analysis and Cox proportional hazards regression. The effect of renal relapse on time to ESRF was assessed by adding renal relapses to the competing events analysis as a time-varying covariate. Results: The number of patients participating was 535; mean serum creatinine (±s.d.) at entry was 341 ± 321 µmol/l and 19.7% developed ESRF. One or more renal relapse(s) was experienced by 101 patients. Multivariable regression analysis demonstrated that, in addition to impaired baseline renal function, developing ⩾1 renal relapse was an independent risk factor for ESRF (subhazard ratio 9; 95% CI 4, 19; P < 0.001). No predictive factors for renal relapse were found. Conclusion: In addition to baseline renal function, the occurrence of renal relapses is an important determinant of ESRF in patients with ANCA-associated vasculitis. We did not find any clinical predictors for renal relapse itself, including disease activity elsewhere. In light of the silent nature of renal relapse in ANCA-associated vasculitis, we stress the need for long-term vigilant monitoring for early signs of renal relapse and propose performing 3-monthly urinalysis. This will enable timely treatment and help further improve renal outcome.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".