The effectiveness and safety of leflunomide in the treatment of giant cell arteritis: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Objectives The objective of this systematic review is to assess the effectiveness and safety of leflunomide in the treatment of new-onset refractory or relapsing giant cell arteritis (GCA) as a glucocorticoid (GC)-sparing agent. Methods We searched MEDLINE, Cochrane Library, Embase, clinical trial registries and other grey literature sources for randomized controlled trials, cohort studies, case-control studies and case series that reported on the use of leflunomide in GCA. The primary effectiveness outcome was the incidence (proportion) of patients who attained sustained GC-free remission at 6 to12 months, defined by the absence of signs or symptoms of GCA, and/or normalization of inflammatory markers, and/or radiologic response, plus complete discontinuation of GC. The secondary outcomes were remission on low-dose GC and adverse events. There was no available comparator. We performed a meta-analysis using a random effects model. Included studies were appraised for risk of bias. Results Of 366 screened studies, 11 observational studies were included in the analysis, pooling data from 358 patients. The pooled proportion of patients achieving sustained GC-free remission was 45% (95% CI 25–64, P < 0.001), with high heterogeneity I2 test = 90.3% (Q = 70.65, P < 0.001). The pooled proportion of patients achieving sustained low-dose GC remission was 48% (95% CI 27–69, P < 0.001) and adverse events occurred in 39% of patients (95% CI 23–44, P < 0.001). All the included studies were deemed to be at high risk of bias. Conclusion Leflunomide’s utility as a GC-sparing agent is promising but remains to be elucidated in future higher-quality studies. PROSPERO protocol registration CRD42023490373.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.033 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".