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 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 | 0.078 | 0.080 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.024 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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 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".