SAFETY AND TOLERABILITY OF TERIFLUNOMIDE IN CLINICAL STUDIES
Bibliographic record
Abstract
Introduction Teriflunomide, approved for the treatment of relapsing-remitting multiple sclerosis, has a well-characterized safety profile based on individual clinical studies. We report pooled safety and tolerability data from four, double-blind, placebo-controlled trials of teriflunomide. Post-approval updates on hair thinning and pregnancy outcomes, sometimes concerns for patients initiating teriflunomide, are reported. Methods Data were pooled from phase 2 ( NCT01487096 ) and phase 3 TEMSO ( NCT00134563 ), TOWER ( NCT00751881 ), and TOPIC ( NCT00622700 ) studies. Patients were randomized to receive teriflunomide 14 mg, 7 mg, or placebo. Safety analyses were performed for all patients exposed to teriflunomide. Results The pooled dataset included 3044 patients. Commonly reported adverse events (AEs) were in accordance with individual clinical studies, most being transient and mild-to-moderate in intensity. Incidence of hepatic AEs was higher in teriflunomide groups; however, serious hepatic AEs were similar across groups (∼2–3%). Hair thinning was higher in teriflunomide than placebo groups, but typically resolved on treatment without intervention and led to discontinuation in <2% of patients. No structural or functional abnormalities were reported in 42 newborns from teriflunomide-exposed parents. Conclusions These data from >6800 patient-years of teriflunomide exposure were consistent with individual studies and no new, unexpected safety signals were observed. (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 | 0.026 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".