Efficacy and Safety of Teriflunomide in Multiple Sclerosis across Age Groups: Analysis from Pooled Pivotal and Real-world Studies
Bibliographic record
Abstract
BACKGROUND: Evidence suggests that efficacy and safety of disease-modifying treatments for multiple sclerosis may differ with age. We evaluate efficacy and safety of teriflunomide across age subgroups of patients from pooled clinical trials and real-world studies. METHODS: 1126) studies were conducted. Data were stratified by age at study entry: ⩽25, >25 to ⩽35, >35 to ⩽45, and >45 years. In Teri-PRO and TAURUS-MS I, an additional group, >55 years, was assessed. RESULTS: In the pooled core studies, teriflunomide reduced annualized relapse rate (ARR) versus placebo across all ages. Unadjusted ARRs remained low across age groups in pooled extensions (0.18-0.30), Teri-PRO (0.10-0.35), and TAURUS-MS I (0.14-0.35). Baseline Expanded Disability Status Scale scores were higher with age, but stable through core and extension studies (mean increases over 7 years: ⩽25 years, +0.59; >25 to ⩽35 years, +0.46; >35 to ⩽45 years, +0.35; >45 years, +0.81). Across age groups, adverse event (AE) incidences were 78.4% to 90.7% in pooled core and extension studies and Teri-PRO, and 29.2% to 37.7% in TAURUS-MS I; serious AE incidences were ⩽21.3% in all studies. In pooled phase III and Teri-PRO studies, lymphocyte count decreases over 1 year after initiating teriflunomide, and proportions of patients developing lymphopenia, were small across age groups. CONCLUSIONS: Teriflunomide efficacy was demonstrated regardless of age. Safety was generally consistent across age groups.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".