De-escalation of Disease-Modifying Therapy for People with Multiple Sclerosis Due to Safety Considerations: Characterizing 1-Year Outcomes in 25 People Who Switched from Ocrelizumab to Diroximel Fumarate
Bibliographic record
Abstract
Switching disease-modifying therapy (DMT) may be considered for relapsing–remitting multiple sclerosis (RRMS) if a patient’s current therapy is no longer optimal. This was particularly important during the recent COVID-19 pandemic because of considerations around immune deficiency and impaired vaccine response associated with B cell-depleting DMTs. This real-world, single-center study aimed to evaluate change or decline in functional ability and overall disease stability in people with RRMS who were switched from B cell-depleting ocrelizumab (OCRE) to diroximel fumarate (DRF) because of safety concern related to the COVID-19 pandemic. Adults with RRMS were included if they had been clinically stable for ≥ 1 year on OCRE. Data collected at baseline and 1 year post switch included relapse rate, magnetic resonance imaging (MRI), blood work for assessment of peripheral immune parameters, the Cognitive Assessment Battery (CAB), optical coherence tomography (OCT), and patient-reported outcomes (PROs). Participants ( N = 25) had a mean (SD) age of 52 (9) years, and a mean (SD) duration of 26 (8) months’ treatment with OCRE before the switch to DRF. Median washout duration since the last OCRE infusion was 7 months (range 4–18 months). No participants relapsed on DRF during follow-up, and all remained persistent on DRF after 1 year. There were no significant changes in peripheral immune parameters, other than an increase in the percentage of CD19 + cells 1 year after switching ( p < 0.05). Similarly, there were no significant changes in CAB, OCT, and PROs. These preliminary findings suggest that transition to DRF from OCRE may be an effective treatment option for people with RRMS who are clinically stable but may need to switch for reasons unrelated to effectiveness. Longer follow-up times on larger samples are needed to confirm these observations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".