Teriflunomide versus subcutaneous interferon beta-1a in patients with relapsing multiple sclerosis: a randomised, controlled phase 3 trial
Bibliographic record
Abstract
BACKGROUND: In previous studies, teriflunomide significantly reduced the annualised relapse rate (ARR) and disability progression. OBJECTIVE: This phase 3, rater-blinded study (NCT00883337) compared teriflunomide with interferon-beta-1a (IFNβ-1a). METHODS: Patients with relapsing multiple sclerosis were randomised (1:1:1) to oral teriflunomide 7-or 14 mg, or subcutaneous IFNβ-1a 44 µg. The primary composite endpoint was time to failure, defined as first occurrence of confirmed relapse or permanent treatment discontinuation for any cause. Secondary endpoints included ARR, Fatigue Impact Scale (FIS) and Treatment Satisfaction Questionnaire for Medication (TSQM). The study was completed 48 weeks after the last patient was randomised. RESULTS: Some 324 patients were randomised (IFNβ-1a: 104; teriflunomide 7 mg: 109; teriflunomide 14 mg: 111). No difference in time to failure was observed. There was no difference in ARR between teriflunomide 14 mg and IFNβ-1a, but ARR was significantly higher with teriflunomide 7 mg. FIS scores indicated more frequent fatigue with IFNβ-1a, though differences were only significant with teriflunomide 7 mg. TSQM scores were significantly higher with teriflunomide. There were no unexpected safety findings. CONCLUSION: Effects on time to failure were comparable between teriflunomide and IFNβ-1a. There was no difference between teriflunomide 14 mg and IFNβ-1a on ARR, though ARR was higher with teriflunomide 7 mg. The teriflunomide safety profile was consistent with previous studies.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".