Magnetic resonance imaging outcomes from a phase III trial of teriflunomide
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to determine the effects of oral teriflunomide on multiple sclerosis (MS) pathology inferred by magnetic resonance imaging (MRI). METHODS: Patients (n=1088) with relapsing MS were randomized to once-daily teriflunomide 7 mg or 14 mg, or placebo, for 108 weeks. MRI was recorded at baseline, 24, 48, 72 and 108 weeks. Annualized relapse rate and confirmed progression of disability (sustained ≥12 weeks) were the primary and key secondary outcomes. The principal MRI outcome was change in total lesion volume. RESULTS: After 108 weeks, increase in total lesion volume was 67.4% (p=0.0003) and 39.4% (p=0.0317) lower in the 14 and 7 mg dose groups versus placebo. Other measures favoring teriflunomide were accumulated enhanced lesions, combined unique activity, T2-hyperintense and T1-hypointense component lesion volumes, white matter volume, and a composite MRI score; all were significant for teriflunomide 14 mg and most significant for 7 mg versus placebo. CONCLUSIONS: Teriflunomide provided benefits on brain MRI activity across multiple measures, with a dose effect evident on several markers. These effects were also consistent across selected subgroups of the study population. These findings complement clinical data showing significant teriflunomide-related reductions in relapse rate and disease progression, and demonstrate containment of MRI-defined disease progression.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".