Efficacy of leflunomide in the treatment of vasculitis
Bibliographic record
Abstract
OBJECTIVES: Only a few small case series, case reports, and one small clinical trial suggested some benefit of leflunomide (LEF) in anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis and other vasculitides. We analysed the clinical efficacy and tolerability of LEF in a large cohort of patients with various vasculitides. METHODS: This was a retrospective analysis of patients who received LEF for treatment of their vasculitis enrolled in the Vasculitis Clinical Research Consortium (VCRC) Longitudinal Study and in 3 additional centres from the Canadian vasculitis research network (CanVasc). RESULTS: Data for 93 patients were analysed: 45 had granulomatosis with polyangiitis (GPA), 8 microscopic polyangiitis (MPA), 12 eosinophilic granulomatosis with polyangiitis (EGPA), 14 giant-cell arteritis (GCA), 9 Takayasu's arteritis (TAK), and 5 polyarteritis nodosa (PAN). The main reason for initiation of LEF was active disease (89%). LEF was efficacious for remission induction or maintenance at 6 months for 62 (67%) patients (64% with GCA, 89% with TAK, 80% with PAN, 69% with GPA, 75% with MPA, 33% with EGPA); 20% discontinued LEF before achieving remission because of persistent disease activity. Overall, 22 adverse events (gastrointestinal symptoms being the most common) led to drug discontinuation in 18 (19%) patients, of which 12 stopped LEF before month 6, before showing any benefit in 8/12 of these patients. CONCLUSIONS: Leflunomide can be an effective therapeutic option for various vasculitides, especially for non-severe refractory or relapsing ANCA-associated vasculitis or large-vessel vasculitis. No new safety signals for LEF were identified in this population.
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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".