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 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 | 0.092 | 0.070 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.026 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".