Immunomodulatory therapy of chikungunya arthritis: systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Chikungunya virus (CHIKV) infection can lead to chronic musculoskeletal complications, including persistent arthritis that resembles autoimmune inflammatory conditions. These symptoms have been reported both in endemic populations and in international travellers, with substantial functional and economic consequences. OBJECTIVE: This systematic review and meta-analysis aimed to assess the efficacy and safety of immunomodulatory therapies in chikungunya-related arthritis. METHODS: A systematic search was conducted in PubMed, Scopus, Cochrane, Web of Science, SciELO and LILACS. Risk of bias was assessed using RoB 2.0 for randomized trials, the Newcastle-Ottawa Scale for observational studies and the Joanna Briggs Institute Checklist for case series. A random-effects meta-analysis was performed due to significant heterogeneity (I2 > 97%).Results: Eleven studies comprising 742 patients met the inclusion criteria. Methotrexate (MTX) was the most studied immunomodulator. The meta-analysis showed a mean reduction in disease activity score of 2.67 (95% CI: 1.84-3.49, P < 0.001, I2 = 97.0%) and a decrease in Visual Analogue Scale pain scores of 4.31 (95% CI: 2.56-6.06, P < 0.001, I2 = 99.1%). Subgroup analysis suggested greater pain reduction in short-term studies. No severe adverse events were reported, but long-term safety data are limited. CONCLUSIONS: Immunomodulatory therapy, particularly MTX, may provide symptom relief in chikungunya arthritis. These findings are relevant not only for endemic settings but also for travel medicine, as chikungunya-related arthritis has been increasingly reported in travellers. However, high study heterogeneity and the lack of randomized trials limit definitive conclusions. Future research should focus on standardizing outcome measures, biomarker-driven patient selection and long-term safety assessments. Clinicians treating returning travellers with chronic joint symptoms should consider post-CHIKV arthritis as part of the differential diagnosis and be aware of potential treatment options.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.033 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".