Comparison of fingolimod, dimethyl fumarate and teriflunomide for multiple sclerosis
Bibliographic record
Abstract
OBJECTIVE: Oral immunotherapies have become a standard treatment in relapsing-remitting multiple sclerosis. Direct comparison of their effect on relapse and disability is needed. METHODS: We identified all patients with relapsing-remitting multiple sclerosis treated with teriflunomide, dimethyl fumarate or fingolimod, with minimum 3-month treatment persistence and disability follow-up in the global MSBase cohort study. Patients were matched using propensity scores. Three pairwise analyses compared annualised relapse rates and hazards of disability accumulation, disability improvement and treatment discontinuation (analysed with negative binomial models and weighted conditional survival models, with pairwise censoring). RESULTS: The eligible cohorts consisted of 614 (teriflunomide), 782 (dimethyl fumarate) or 2332 (fingolimod) patients, followed over the median of 2.5 years. Annualised relapse rates were lower on fingolimod compared with teriflunomide (0.18 vs 0.24; p=0.05) and dimethyl fumarate (0.20 vs 0.26; p=0.01) and similar on dimethyl fumarate and teriflunomide (0.19 vs 0.22; p=0.55). No differences in disability accumulation (p≥0.59) or improvement (p≥0.14) were found between the therapies. In patients with ≥3-month treatment persistence, subsequent discontinuations were less likely on fingolimod than teriflunomide and dimethyl fumarate (p<0.001). Discontinuation rates on teriflunomide and dimethyl fumarate were similar (p=0.68). CONCLUSION: The effect of fingolimod on relapse frequency was superior to teriflunomide and dimethyl fumarate. The effect of the three oral therapies on disability outcomes was similar during the initial 2.5 years on treatment. Persistence on fingolimod was superior to the two comparator drugs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".