Temporal profile of lymphocyte counts and relationship with infections with fingolimod therapy
Bibliographic record
Abstract
BACKGROUND: Reduction in peripheral blood lymphocytes is an expected pharmacodynamic outcome of fingolimod therapy. OBJECTIVE: The objective of this article is to evaluate lymphocyte dynamics during and after fingolimod therapy and assess the relationship between lymphocyte counts and infections. METHODS: Lymphocyte counts and their relationship with infections were evaluated in three multiple sclerosis (MS) populations: (Group A) FREEDOMS phase 3 core study group (n = 1272); (Group B) All Studies group (one phase 2 and two phase 3 studies, plus their extensions; n = 2315); and (Group C) Follow-up group (after fingolimod discontinuation; n = 538). RESULTS: Administration of fingolimod 0.5 mg led to reductions in lymphocyte counts to a steady-state of 24%-30% of baseline values within two weeks, which remained stable while on therapy. Following fingolimod discontinuation, average counts exceeded the lower limit of normal range within six to eight weeks, and were 80% of baseline values by three months. In Group A, infection rates per patient-year were 1.4 with placebo and 1.0 in fingolimod-treated patients who had the lowest lymphocyte counts (< 0.2 × 10(9)/l). No evidence was seen for an increase in serious or opportunistic infections. CONCLUSIONS: Fingolimod induces a rapid and reversible reduction in lymphocyte counts without an increase in infections relative to placebo. Because fingolimod reduces blood lymphocyte counts via redistribution in secondary lymphoid organs, peripheral blood lymphocyte counts cannot be utilized to evaluate the lymphocyte subset status of a patient.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".