EFFECT OF TERIFLUNOMIDE ON LYMPHOCYTE AND NEUTROPHIL COUNTS
Bibliographic record
Abstract
Introduction Teriflunomide is an immunomodulator known to decrease the proliferation of stimulated lymphocytes via inhibition of dihydro-orotate dehydrogenase. Lymphocyte/neutrophil counts were assessed in pooled data from one phase 2 and three phase 3 (TEMSO, TOWER, and TOPIC) placebo-controlled studies. Methods Patients were randomized to receive once-daily teriflunomide 14mg (n=1002), 7mg (n=1045), or placebo (n=997). Blood samples were collected throughout the studies. Results Mean baseline lymphocyte and neutrophil counts were similar across groups. Small decreases in mean lymphocyte and neutrophil counts occurred within the first 12 weeks (lymphocytes) or 6 weeks (neutrophils) of treatment, and stabilized within the normal range for most patients thereafter. Patients with neutrophil counts <1×10^9^/L were to discontinue treatment; 11 (1.1%; 14 mg), 8 (0.8%; 7 mg), and 1 (0.1%; placebo) patients discontinued due to neutropenia or neutrophil count decrease as per protocol requirements. Neutropenia was reported as a serious adverse event (SAE) in 7 (0.7%; 14 mg), 2 (0.2%; 7 mg), and 3 (0.3%; placebo) patients; there were no lymphopenia SAEs. No link between neutrophil or lymphocyte count decreases and infection was observed. Conclusions These data demonstrate that teriflunomide has small, reversible effects on lymphocyte/neutrophil counts, with no increase in infection risk 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".