Leflunomide Use and Risk of Lung Disease in Rheumatoid Arthritis: A Systematic Literature Review and Metaanalysis of Randomized Controlled Trials
Bibliographic record
Abstract
OBJECTIVE: To evaluate the relative risk (RR) of pulmonary disease among patients with rheumatoid arthritis (RA) treated with leflunomide (LEF). METHODS: We searched PubMed, Embase, and the Cochrane Central Register of Controlled Trials to April 15, 2014. We included double-blind randomized controlled trials (RCT) of LEF versus placebo or active comparator agents in adults with RA. Studies with fewer than 50 subjects or shorter than 12 weeks were excluded. Two investigators independently searched both databases. All authors reviewed selected studies. We compared RR differences using the Mantel-Haenszel random-effects method to assess total respiratory adverse events, infectious respiratory adverse events, noninfectious respiratory adverse events, interstitial lung disease, and death. RESULTS: Our literature search returned 5673 results. A total of 8 studies, 4 with placebo comparators, met our inclusion criteria. There were 708 respiratory adverse events documented in 4579 participants. Six cases of pneumonitis occurred, all in the comparator group. Four pulmonary deaths were reported, none in the LEF group. LEF was not associated with an increased risk of total adverse respiratory events (RR 0.99, 95% CI 0.56-1.78) or infectious respiratory adverse events (RR 1.02, 95% CI 0.58-1.82). LEF was associated with a decreased risk of noninfectious respiratory adverse events (RR 0.64, 95% CI 0.41-0.97). CONCLUSION: Our study found no evidence of increased respiratory adverse events in RCT of LEF treatment.
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 | 0.025 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.032 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".