Alemtuzumab-Related Lymphocyte Subset Dynamics and Disease Activity or Autoimmune Adverse Events: Real-World Evidence
Bibliographic record
Abstract
Background and objectives: alemtuzumab is a monoclonal anti-CD52 antibody acting on B and T cells in highly active multiple sclerosis (MS). We analyzed changes in lymphocyte subsets after alemtuzumab administration in relation to disease activity and autoimmune adverse events. Methods: lymphocyte subset counts were assessed longitudinally using linear mixed models. Subset counts at baseline and during follow-up were correlated with relapse rate, adverse events, or magnetic resonance (MRI) activity. Results: we recruited 150 patients followed for a median of 2.7 years (IQR: 1.9–3.7). Total lymphocytes, CD4, CD8, and CD20 significantly decreased in all patients over 2 years (p < 0.001). Previous treatment with fingolimod increased the risk of disease activity and adverse events (p = 0.029). We found a higher probability of disease reactivation in males and in patients with over three active lesions at baseline. Higher EDSS scores at baseline and longer disease duration predicted the switch to other treatments after alemtuzumab. Discussion and conclusions: Our real-world study supports data from clinical trials in which lymphocyte subsets were not useful for predicting disease activity or autoimmune disease during treatment. The early use of an induction therapy such as alemtuzumab in patients with a lower EDSS score and short history of disease could mitigate the risk of treatment failure.
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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 | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".