Teriflunomide in pediatric patients with relapsing multiple sclerosis: Open-label extension of TERIKIDS
Bibliographic record
Abstract
Background: The double-blind TERIKIDS study demonstrated the efficacy and safety of teriflunomide. Objective: To evaluate the efficacy, safety, and tolerability of continuous teriflunomide treatment in the TERIKIDS open-label extension. Methods: In the double-blind period, children with relapsing MS were randomized to placebo or teriflunomide (14 mg adult-equivalent dose) for ⩽ 96 weeks. Participants received teriflunomide for ⩽ 192 weeks post-randomization in the open-label extension. Results: The mean age at screening was 14.6 years. For teriflunomide/teriflunomide versus placebo/teriflunomide, estimated clinical relapse risk was reduced by 38% (hazard ratio (HR) 0.62; 95% confidence interval (CI) 0.39–0.98; p = 0.11) and numbers of gadolinium-enhancing T1 and new/enlarging T2 lesions were reduced by 43% (relative risk (RR) 0.570; 95% CI 0.33–0.98; p = 0.043) and 49% (RR 0.511; 95% CI 0.34–0.76; p = 0.001), respectively, in the combined double-blind and open-label periods. There was a trend toward reduced risk of 24-week sustained disability progression for teriflunomide/teriflunomide versus placebo/teriflunomide (HR 0.47; 95% CI 0.23–0.96). During the open-label extension, incidences of safety-related discontinuations were 4.0% (teriflunomide/teriflunomide) and 13.5% (placebo/teriflunomide), including two children who developed pancreatitis in the teriflunomide/teriflunomide group. Conclusion: Teriflunomide reduced the long-term risk of focal inflammatory activity, with generally manageable tolerability and no new safety signals. Further evidence would strengthen clinical efficacy findings. ClinicalTrials.gov: NCT02201108.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".