Patients Free of Clinical MS Activity in TEMSO and TOWER: Pooled Analyses of Two Phase 3 Placebo-Controlled Trials (P3.164)
Bibliographic record
Abstract
OBJECTIVE: To determine the effect of teriflunomide on reducing risk of clinical multiple sclerosis (MS) activity, along with effect in pre-defined subgroups, based on pooled TEMSO (NCT00134563) and TOWER (NCT00751881) data. BACKGROUND: Teriflunomide is an oral immunomodulator approved in several countries for treatment of relapsing-remitting MS. Teriflunomide has shown consistent beneficial effects_reducing disease activity (relapse and MRI) and slowing disability progression, with a manageable safety profile across all studies. DESIGN/METHODS: A total of 2251 patients 蠅18 years with relapsing forms of MS (RMS) were randomized and treated (modified intent-to-treat population) with once-daily teriflunomide 14mg (n=728) or 7mg (n=772), or placebo (n=751) for 108 weeks (TEMSO) or 48 weeks after last patient randomized (TOWER). Primary and key secondary endpoints were annualized relapse rate (ARR) and disability progression sustained for 12 weeks. Analyses were performed according to pre-specified subgroups defined by baseline disease characteristics and prior disease-modifying therapy use. Post hoc analysis evaluated the proportion of patients free of clinical MS activity (composite of patients free of clinical relapse and disability progression sustained for 12 weeks). MRI was not performed in TOWER. RESULTS: Across treatment groups, patient demographics and baseline disease characteristics were well balanced. There was no evidence of a differential treatment effect across levels of the pre-specified subgroups for ARR or disability progression (14mg). Both teriflunomide doses significantly delayed time to clinical MS activity vs placebo (p<0.0001 and p=0.0005, for 14mg and 7mg, respectively). The proportion of patients free of clinical MS activity (Week 108) was 50.8%, 48.1%, and 40.7% for 14mg, 7mg, and placebo, respectively. Corresponding relative rate reductions (95% confidence intervals) were 29.2% (17.9%, 38.9%) for 14mg and 22.8% (11.0%, 33.1%) for 7mg versus placebo. CONCLUSIONS: Pooled efficacy and subgroup analyses show that teriflunomide significantly reduced risk of clinical MS activity and demonstrated consistent efficacy in a broad range of patients with RMS, confirming results of the individual studies. Study Supported by: Genzyme, a Sanofi company.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.017 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".