An exploratory analysis of the efficacy of ocrelizumab in patients with multiple sclerosis with increased disability
Bibliographic record
Abstract
BACKGROUND: Ocrelizumab, an anti-CD20 humanized monoclonal antibody, reduced disease progression in pivotal trials of patients with relapsing (OPERA I, OPERA II) and primary progressive (ORATORIO) multiple sclerosis (MS). These effects may be particularly important among patients with increased disability. OBJECTIVE: In this post hoc exploratory analysis, we evaluated the efficacy of ocrelizumab on disability progression among a subgroup of patients with MS who had increased baseline disability levels (Expanded Disability Status Scale scores ≥4.0) in the pivotal trials. METHODS: During the double-blind period, patients received ocrelizumab 600 mg intravenously every 24 weeks for 96 weeks in the OPERA trials (versus interferon β-1a 44 μg subcutaneously three times per week) and for 120 weeks in ORATORIO (versus placebo). Kaplan-Meier and Cox survival analyses were used to assess disability outcome measures. RESULTS: Baseline demographic, disease, and treatment characteristics were generally comparable across treatment groups in patients with increased disability from the OPERA and ORATORIO trials. Ocrelizumab treatment numerically, and in some instances significantly, reduced confirmed disability progression versus the comparator in these patients. CONCLUSIONS: In patients with increased baseline disability, ocrelizumab reduced the risk of confirmed disability progression versus interferon β-1a in patients with relapsing-onset MS and versus placebo in patients with progression-onset MS.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".