3549 Continuous ofatumumab treatment up to 7 years shows a consistent safety profile and delays disability progression in people with RMS
Bibliographic record
Abstract
Background/ Objectives Previously reported data up to 6 years of ofatumumab treatment demonstrated a favourable safety profile and sustained efficacy. Here we describe long-term safety of ofatumumab and assess disability outcomes (up to 7 years) of early initiation of ofatumumab treatment versus delayed treatment (after switching from teriflunomide) in people with relapsing multiple sclerosis (pwRMS).Methods Safety analyses include participants who received ≥1 dose of ofatumumab in ASCLEPIOS I/II, APOLITOS, APLIOS, or ALITHIOS. Efficacy analyses evaluate cumulative data up to 7 years (cutoff: 25-Sep-2024) from pwRMS randomized to ofatumumab or teriflunomide in ASCLEPIOS I/II, regardless of whether they entered the ALITHIOS open-label extension phase. Event rates of 3/6-month (m) confirmed disability worsening (3/6mCDW), progression independent of relapse activity (3/6mPIRA; CDW events without prior confirmed relapses), and relapse-associated disability worsening (3/6mRAW; disability onset <90 days from relapse) will be assessed.Results Exposure-adjusted incidence rates of adverse events (AEs), serious AEs, serious infections, and malignancies remained low and consistent, with no increased risk over 6 years. Previously reported 6-year data (cutoff: 25-Sep-2023) showed Kaplan-Meier cumulative event rates were numerically lower in pwRMS receiving continuous ofatumumab in ASCLEPIOS I/II and ALITHIOS (OMB-OMB) versus delayed treatment (TER-OMB) for 6mCDW (21.1% vs 24.8%, p=0.063), 6mPIRA (15.5% vs 16.6%), and 6mRAW (5.2% vs 5.8%). Updated 7-year safety and efficacy data will be presented at the congress.Conclusion These analyses will further support long-term safety and efficacy data for ofatumumab in pwRMS, including RDTN pwRMS, informing clinical decision-making.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.006 | 0.001 |
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".