Safety of the Methotrexate–leflunomide Combination in Rheumatoid Arthritis: Results of a Multicentric, Registry-based, Cohort Study (BiobadaBrasil)
Bibliographic record
Abstract
Objective. To evaluate the safety of the methotrexate (MTX)–leflunomide (LEF) combination in rheumatoid arthritis (RA), comparing it with other therapeutic schemes involving conventional synthetic (cs-) and biologic (b-) disease-modifying antirheumatic drugs (DMARDs) or Janus kinase inhibitors (JAKi). Methods. Patients with RA starting a treatment course with a csDMARD (without previous use of bDMARD or JAKi) or their first bDMARD/JAKi were followed up in a registry-based, multicentric cohort study in Brazil (BiobadaBrasil). The primary outcome was the incidence of serious adverse events (SAEs); secondary outcomes included serious infections. Multivariate Cox proportional hazards models and propensity score matching analysis (PSMA) were used for statistical comparisons. Results. In total, 1671 patients (5349 patient-years [PY]) were enrolled; 452 patients (1537 PY) received MTX + LEF. The overall incidence of SAEs was 5.6 per 100 PY. The hazard of SAEs for MTX + LEF was not higher than for MTX or LEF (adjusted HR [aHR] 1.00, 95% CI 0.76–1.31, P = 0.98). MTX + LEF presented a lower hazard of SAEs (aHR 0.56, 95% CI 0.36–0.88, P = 0.01) and infectious SAEs (aHR 0.48, 95% CI 0.25–0.94, P = 0.03) than bDMARDs/JAKi with MTX or LEF. MTX + LEF presented lower hazard of SAEs than MTX + sulfasalazine (SSZ; aHR 0.33, 95% CI 0.16–0.65, P = 0.002). Analysis using PSMA confirmed the results obtained with traditional multivariate Cox analysis. Conclusion. In our study, MTX + LEF presented a relatively good overall safety profile in comparison to MTX + SSZ and schemes involving advanced therapies in RA.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".