Characterizing lymphocyte counts and infection rates with long-term teriflunomide treatment: Pooled analysis of clinical trials
Bibliographic record
Abstract
BACKGROUND: In Phase 3 studies, teriflunomide reduced relapse rates and disability progression compared with placebo; however, decreases in lymphocyte counts were also observed. OBJECTIVE: To describe the effect of long-term teriflunomide treatment on lymphocyte counts and infection rates among patients in pooled analyses of Phase 3 core and extension studies. METHODS: Four randomized trials (TEMSO, TOWER, TENERE, and TOPIC) compared teriflunomide 7 mg or 14 mg treatment with either placebo and/or subcutaneous interferon (IFN) β-1a 44 µg in patients with relapsing forms of multiple sclerosis (MS) (or first clinical episode suggestive of MS in TOPIC). RESULTS: In 1895, patients ever exposed to teriflunomide, mean (standard deviation) absolute lymphocyte counts declined from Week 0 (1.89 (0.59)) to Week 24 (1.67 (0.52)) and then remained stable thereafter. In the core plus extension studies (up to 10.7 years), 7.3% and 2.2% experienced Grade 1 and Grade 2 lymphopenia, respectively. Infections were reported in 56.9% of patients without lymphopenia, 60.9% with Grade 1 lymphopenia, and 54.8% with Grade 2 lymphopenia. Serious infections occurred in 3.7%, 4.3%, and 7.1%, respectively. CONCLUSION: Long-term risk of lymphopenia and infections in patients who continue to receive teriflunomide is low, demonstrating a limited impact on adaptive and innate immunity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".