No evidence of disease activity (NEDA) analysis by epochs in patients with relapsing multiple sclerosis treated with ocrelizumab vs interferon beta-1a
Bibliographic record
Abstract
Background No evidence of disease activity (NEDA; defined as no 12-week confirmed disability progression, no protocol-defined relapses, no new/enlarging T2 lesions and no T1 gadolinium-enhancing lesions) using a fixed-study entry baseline is commonly used as a treatment outcome in multiple sclerosis (MS). Objective The objective of this paper is to assess the effect of ocrelizumab on NEDA using re-baselining analysis, and the predictive value of NEDA status. Methods NEDA was assessed in a modified intent-to-treat population ( n = 1520) from the pooled OPERA I and OPERA II studies over various epochs in patients with relapsing MS receiving ocrelizumab (600 mg) or interferon beta-1a (IFN β‐1a; 44 μg). Results NEDA was increased with ocrelizumab vs IFN β-1a over 96 weeks by 75% ( p < 0.001), from Week 0‒24 by 33% ( p < 0.001) and from Week 24‒96 by 72% ( p < 0.001). Among patients with disease activity during Weeks 0‒24, 66.4% vs 24.3% achieved NEDA during Weeks 24‒96 in the ocrelizumab and IFN β-1a groups (relative increase: 177%; p < 0.001). Conclusion Superior efficacy with ocrelizumab compared with IFN β-1a was consistently seen in maintaining NEDA status in all epochs evaluated. By contrast with IFN β-1a, the majority of patients with disease activity early in the study subsequently attained NEDA status with ocrelizumab.
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".