Tolerability of subcutaneous ofatumumab with long-term exposure in relapsing multiple sclerosis
Bibliographic record
Abstract
Background: Ofatumumab is approved for treating relapsing multiple sclerosis (RMS). Examining tolerability will enable understanding of its risk-benefit profile. Objective: Report the tolerability profile of ofatumumab in RMS during treatment of up to 4 years and the effect of pre-medication. Methods: Cumulative data from the overall safety population included patients taking continuous ofatumumab or being newly switched from teriflunomide. Injection-related reactions (IRRs) by incidence and severity, and post-marketing surveillance data, with an exposure of 18,530 patient-years, were analyzed. Results: Systemic IRRs affected 24.7% of patients (487/1969) in the overall safety population; most (99.2% [483/487]) were mild (333/487) to moderate (150/487) in Common Terminology Criteria for Adverse Events severity; most systemic IRRs occurred after first injection. Local-site IRRs affected 11.8% (233/1969) and most (99.6% [232/233]) were mild/moderate. Incidence and severity of systemic and localized IRRs were similar between continuous and newly switched patients across repeated injections. Systemic IRR incidence and severity were not substantially affected by steroidal or non-steroidal pre-medication. Post-marketing surveillance identified no new tolerability issues. Conclusion: Ofatumumab is well tolerated, displays a consistent safety profile during continuous use or after switching from teriflunomide and does not require pre-medication. This enables home management of RMS with a high-efficacy treatment.
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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 | 0.002 |
| 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) | 0.001 | 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".