P.016 Secondary immunodeficiencies in ocrelizumab - versus rituximab-treated persons with relapsing Multiple Sclerosis
Bibliographic record
Abstract
Background: Anti-CD20 monoclonal antibodies are highly effective for RMS treatment. Ocrelizumab (OCR) is standard, while Rituximab (RTX) is an alternative. The impact of anti-CD20 therapies on immune markers remains understudied, though deficiencies are frequently observed and have been associated with increased risk of infection. Our objective is to characterize and compare lymphocyte, neutrophil, and immunoglobulin levels in OCR- versus RTX-treated persons with RMS. Methods: This retrospective chart review included RMS patients on OCR or RTX (2017–2023). Pre- and post-treatment levels of lymphocytes, neutrophils, and immunoglobulins (IgG, IgA, IgM) were analyzed. Kaplan-Meier curves, log-rank tests, and Cox proportional hazards models were used for survival analysis. Results: 350 patients (OCR=175, RTX=175) were included. The mean treatment length was 60.9 (SD 19.1) months for OCR and 42.7 (SD 19.5) months for RTX. RTX was associated with a significantly shorter time to IgM deficiency (29.6 vs. 40.0 months, p=0.02). Cox analysis confirmed RTX increased IgM deficiency risk (HR=1.54, 95% CI: 1.06-2.23, p=0.02). No differences were seen for lymphocytes, neutrophils, IgG, or IgA. Conclusions: RTX was associated with a shorter time to and increased risk of IgM hypogammaglobulinemia compared to OCR, highlighting the importance of long-term monitoring. Further research is needed to guide treatment decisions.
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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) | 0.000 | 0.001 |
| 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) | 0.003 | 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".