Prior treatment status: impact on the efficacy and safety of teriflunomide in multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: In this pooled, post hoc analysis of a phase 2 trial and the phase 3 TEMSO, TOWER, and TENERE clinical trials, long-term efficacy and safety of teriflunomide were assessed in subgroups of patients with relapsing multiple sclerosis (MS) defined by prior treatment status. METHODS: Patients were classified according to their prior treatment status in the core and core plus extension periods. In the core period, patients were grouped according to treatment status at the start of the study: treatment naive (no prior disease-modifying therapy [DMT] or DMT > 2 years prior to randomization), previously treated with another DMT (DMT > 6 to ≤24 months prior to randomization), and recently treated with another DMT (DMT ≤6 months prior to randomization). In the core plus extension period, patients were re-baselined to the time of starting teriflunomide 14 mg and grouped according to prior treatment status at that time point. Efficacy endpoints included annualized relapse rate (ARR), probability of confirmed disability worsening (CDW) over 12 weeks, and Expanded Disability Status Scale (EDSS) score. The incidence of adverse events was also assessed. RESULTS: Most frequently received prior DMTs at baseline were glatiramer acetate and interferon beta-1a across treatment groups. Teriflunomide 14 mg significantly reduced ARR versus placebo in the core period, regardless of prior treatment status. In the core and extension periods, adjusted ARRs were low (0.193-0.284) in patients treated with teriflunomide 14 mg across all subgroups. Probability of CDW by Year 4 was similar across subgroups; by Year 5, the percentage of patients with 12-week CDW was similar in treatment-naive patients and patients recently treated with another DMT (33.9 and 33.7%, respectively). EDSS scores were stable over time in all prior-treatment subgroups. There were no new or unexpected safety signals. Limitations include selective bias due to patient attrition, variability in subgroup size, and lack of magnetic resonance imaging outcomes. CONCLUSIONS: The efficacy and safety of teriflunomide 14 mg was similar in all patients with relapsing MS, regardless of prior treatment history. TRIAL REGISTRATION: Phase 2 trial core: NCT01487096 ; Phase 2 trial extension: NCT00228163 ; TEMSO core: NCT00134563 ; TEMSO extension: NCT00803049 ; TOWER: NCT00751881 ; TENERE: NCT00883337 .
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".