Safety and effectiveness of diroximel fumarate in relapsing forms of multiple sclerosis: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the safety and efficacy of Diroximel Fumarate (DRF) in patients with different relapsing forms of MS (RMS) through systematic review and meta-analysis. METHODS: A systematic review and meta-analysis adhering to PRISMA guidelines was conducted. Scopus, PubMed, and Cochrane CENTRAL databases were searched until December 6, 2024, for clinical trials and observational studies on DRF in RMS. Eligibility criteria included studies evaluating DRF's safety or efficacy, excluding case reports and non-clinical outcomes. The risk of bias was assessed using the Newcastle-Ottawa Scale and ROBINS-I tools. Statistical analyses were performed using OpenMetaAnalyst, focusing on pooled mean differences and incidence rates with 95% confidence intervals. RESULTS: Seven studies with 3,075 participants were included. The overall persistence rate was 75.6% (95% CI: 63.5%, 87.7%). The discontinuation rate due to safety concerns was 6.1% (95% CI: 4.1%, 8.1%). Lymphocyte count decreased significantly by -355.02 cells/µL (95% CI: -636.71, -73.32). Mild adverse events (AEs) occurred in 33% (95% CI: 18.6%, 47.4%), moderate in 30% (95% CI: -9.9%, 69.9%), and severe in 5% (95% CI: -3.8%, 13.7%). Gastrointestinal (GI) AEs were observed in 17.4% (95% CI: 6%, 28.8%), flushing in 18.5% (95% CI: 5.7%, 31.3%), and lymphopenia in 24.3% (95% CI: 10.2%, 38.4%). The relapse rate was 7.1% (95% CI: -4.8%, 19%). CONCLUSION: DRF demonstrates efficacy in reducing relapse rates and offers an improved safety profile compared to its predecessor, Dimethyl Fumarate (DMF), particularly in GI tolerability. However, lymphopenia requires monitoring. Further research is recommended to evaluate long-term safety and efficacy in diverse populations.
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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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.048 |
| Bibliometrics | 0.008 | 0.007 |
| 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".