Pre-specified subgroup analyses of a placebo-controlled phase III trial (TEMSO) of oral teriflunomide in relapsing multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: The Teriflunomide Multiple Sclerosis Oral (TEMSO) trial, a randomized, double-blind, placebo-controlled phase III study, demonstrated that teriflunomide significantly reduced annualized relapse rate (ARR), disease progression and magnetic resonance imaging (MRI) activity, with a favorable safety profile in relapsing multiple sclerosis (RMS) patients. OBJECTIVE: The purpose of this study was to report the effects of teriflunomide on ARR and disability progression in pre-specified subgroups. METHODS: RMS patients (n=1088) were randomized to placebo or teriflunomide, 7 mg or 14 mg, once daily, for 108 weeks. Subgroup analyses were performed for ARR and disability progression by baseline demographics (gender, race, age), disease characteristics (Expanded Disability Status Scale (EDSS) strata, relapse history, multiple sclerosis (MS) subtype), MRI parameters (gadolinium-enhancing lesions, total lesion volume) and prior use of MS drugs. A generalized estimating equation method and Cox regression model were used to assess consistency of the treatment effect across subgroups, utilizing a treatment-by-subgroup interaction test for each factor separately. RESULTS: Reductions in ARR and disability progression were consistent across subgroups in favor of teriflunomide, with no treatment-by-subgroup interaction test reaching statistical significance. CONCLUSION: The positive effects of teriflunomide were demonstrated consistently across subgroups in TEMSO.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".