Long-term follow-up of a phase 2 study of oral teriflunomide in relapsing multiple sclerosis: safety and efficacy results up to 8.5 years
Bibliographic record
Abstract
BACKGROUND: Teriflunomide, an oral disease-modifying therapy in development for patients with relapsing forms of multiple sclerosis (RMS), was well tolerated and effective in reducing magnetic resonance imaging (MRI) lesions in 179 RMS patients in a phase 2 36-week, placebo-controlled study. METHODS: A total of 147 patients who completed the core study entered an open-label extension. Teriflunomide patients continued their assigned dose, and placebo patients were re-allocated to teriflunomide, 7 mg/day or 14 mg/day. An interim analysis was performed at a cut-off on January 8 2010. RESULTS: The mean and median duration of study treatment, including both the core and extension phase, from baseline to the interim cut-off, was 5.6 years (standard deviation: 2.7 years) and 7.1 years (range: 0.05-8.5 years), respectively. Of 147 patients, 62 (42.2%) discontinued (19% due to treatment-emergent adverse events (TEAEs)). The most common TEAEs were mild infections, fatigue, sensory disturbances and diarrhoea. No serious opportunistic infections occurred, with no discontinuations due to infection. Asymptomatic alanine aminotransferase increases (≤3× upper limit of normal (ULN)) were common (7 mg, 64.2%; 14 mg, 62.1%); increases >3×ULN were similar across groups (7 mg, 12.3%; 14 mg, 12.1%). Mild decreases in neutrophil counts occurred; none led to discontinuation. The incidence of malignancies was comparable to that of the general population, and cases were not reminiscent of those observed in immunocompromised patients. Annualised relapse rates remained low, minimal disability progression was observed, with a dose-dependent benefit with teriflunomide 14 mg for several MRI parameters. CONCLUSION: Teriflunomide had a favourable safety profile for up to 8.5 years.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".