Safety experience with continued exposure to ofatumumab in patients with relapsing forms of multiple sclerosis for up to 3.5 years
Bibliographic record
Abstract
BACKGROUND: Ofatumumab is approved for the treatment of relapsing multiple sclerosis (RMS). Ongoing safety reporting is crucial to understand its long-term benefit-risk profile. OBJECTIVE: Report the safety and tolerability of ofatumumab in RMS after extended treatment up to 3.5 years. METHODS: Patients completing ASCLEPIOS I/II (phase 3), APLIOS, or APOLITOS (phase 2) trials could enter ALITHIOS, a phase 3b, open-label, long-term safety study. We analyzed cumulative data of continuous ofatumumab treatment and of patients newly switched from teriflunomide. RESULTS: The safety population had 1969 patients: 1292 continuously treated with ofatumumab (median time-at-risk 35.5 months, 3253 patient-years) and 677 newly switched (median time-at-risk 18.3 months, 986 patient-years). A total of 1650 patients (83.8%) had ⩾1 adverse events and 191 (9.7%) had ⩾1 serious adverse events. No opportunistic infections or progressive multifocal leukoencephalopathy events were identified; the risk of malignancies was low. Mean serum immunoglobulin (Ig) G levels remained stable. Mean IgM levels decreased but remained above the lower limit of normal in most. Serious infection incidence was low; decreased Ig levels were not associated with serious infections. CONCLUSION: In patients with up to 3.5 years' exposure, ofatumumab was well tolerated, with no new safety risks identified. These findings, with its established effectiveness, support a favorable benefit-risk profile of ofatumumab in RMS.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".