Comparable Efficacy and Safety of Teriflunomide versus Dimethyl Fumarate for the Treatment of Relapsing-Remitting Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND: The aim of this observational study is to investigate the efficacy and safety of two approved oral disease-modifying therapies (DMTs) in patients with remitting-relapsing multiple sclerosis (RRMS): dimethyl fumarate (DMF) vs. teriflunomide (TRF). METHODS: A total of 159 RRMS patients (82 on TRF and 77 on DMF) were included. The expanded disability status scale (EDSS), confirmed disability improvement (CDI), confirmed disability progression (CDP), and annualized relapse rate (ARR) were evaluated for the two-year period prior to enrollment in our study. The drug-associated adverse effects (AEs) were recorded. We conducted propensity matching score to compare the efficacy between TRF and DMF. RESULTS: value >0.05). TRF discontinuation occurred in 2 patients (2.43%) due to mediastinitis and liver dysfunction, while a patient (1.29%) discontinued DMF due to depression. Incidence rate of AEs in the TRF-treated group was 81.4%: hair thinning (hair loss) (62.9%), nail loss (20.9%), and elevated aminotransferase (14.8%) were the most common AEs; in DMF-treated patients, AEs were 88.2% with predominance of flushing (73.2%), pruritus (16.9%), and abdominal pain (16.9%). CONCLUSION: Based on our findings, DMF is as efficacious and safe as TRF for the treatment of RRMS in our Iranian study population. Multicentric studies need to corroborate these findings in other 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".