The Efficacy and Safety of Leflunomide in the Treatment of Giant Cell Arteritis: A Protocol for a Systematic Review
Bibliographic record
Abstract
Background Giant cell arteritis (GCA) is a type of large vessel vasculitis that causes inflammation, scarring and stenosis in large vessels. Clinical symptoms include headaches, visual changes and myalgias. The standard treatment for GCA consists of glucocorticoid therapy, however long-term use of glucocorticoid therapy poses important health risks. For this reason, there has been a growing interest in exploring other glucocorticoid-sparing therapies, such as leflunomide. The objective of this systematic review is to assess the efficacy and safety of leflunomide in the treatment of GCA. Methods We will include studies with adult patients who received leflunomide in the treatment of GCA. We will include randomized controlled trials, cohort studies, case-control studies, and case series. The intervention of interest is the use of leflunomide, which may be initiated at any time over the course of the disease. The comparator is glucocorticoid therapy alone. The primary efficacy outcome is the incidence (proportion) of patients who attain GC-free remission, as defined by the following: 1) absence of signs or symptoms of GCA, and/or 2) normalization of inflammatory markers, and/or 3) radiologic response, and 4) complete discontinuation of GC. Outcomes will be assessed after 1 year of follow-up. Quality appraisal will be performed using the respective risk of bias tools. We will perform a meta-analysis using a random-effects model if the included studies are sufficiently homogenous. For quantitative synthesis, we will employ Cochran-Mantel-Haenszel method with odds ratios as a measure of effect and 95% confidence intervals for our primary endpoint. Discussion The challenge in the treatment of GCA is attaining and maintaining disease remission with the least amount of glucocorticoid therapy possible. The only approved glucocorticoid-sparing agent in the treatment of large vessel vasculitis is tocilizumab. Leflunomide is a molecule with similar downstream effects on dendritic cells and cytokines as tocilizumab. Should leflunomide demonstrate similar benefits, it would be a cost-effective, safe, and user-friendly (oral route) option. Systematic review registration Our systematic review protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO, registration number CRD42023490373).
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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 | 0.043 | 0.061 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.018 | 0.022 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.033 | 0.003 |
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".