Temporal profile of lymphocyte counts and relationship with infections with fingolimod therapy
Why this work is in the frame
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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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it