A retrospective analysis of changes in lymphocyte levels in patients with multiple sclerosis during and after Tecfidera® treatment
Bibliographic record
Abstract
BACKGROUND: There are currently no best practice recommendations for lymphocyte subset monitoring for patients with multiple sclerosis (pwMS) on disease-modifying therapies including Tecfidera® (dimethyl fumarate, DMF). However, there have been several cases of pwMS on DMF without severe lymphopenia who had high CD4:CD8 T cell ratios and went on to develop progressive multifocal leukoencephalopathy. OBJECTIVE: Our objective was to characterize the changes in immune profile during and after DMF treatment in pwMS. METHODS: A retrospective analysis of longitudinal data from 299 pwMS who have been treated with DMF at the Fraser Health Multiple Sclerosis Clinic in British Columbia, Canada. The blood test results were taken from January 1, 2013 to April 1, 2020. RESULTS: Our results suggest that CD8+ T cells had the highest proportional decrease compared to other lymphocyte subset populations and overall lymphocyte count in response to DMF treatment. CD56+ Natural Killer cells were similarly decreased in response to DMF treatment. CD4:CD8 T cell ratio was the measurement that had the highest rate of change in response to DMF initiation and discontinuation. CONCLUSION: CD8+ T cell count and CD4:CD8 T cell ratio may be a more sensitive measurement of the immune landscape of patients with MS on DMF.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 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 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".