OCRELIZUMAB VS INTERFERON β-1A IN RELAPSING MS: TWO STUDIES
Bibliographic record
Abstract
Introduction B cells are implicated in MS pathophysiology. Ocrelizumab (OCR) is a humanised monoclonal antibody that selectively targets CD20+ B cells. OCR was evaluated in relapsing MS (RMS) in two identical, Phase III, randomised, double-blind, double-dummy, interferon beta-1a (IFNβ-1a)-controlled studies (OPERA I/NCT01247324 and OPERA II/NCT01412333). Methods Eligible RMS patients were randomised 1:1 to receive OCR 600 mg every 24 weeks or IFNβ-1a 44 µg three-times weekly for 96 weeks. The primary endpoint was annualised relapse rate (ARR) by 96 weeks. Key secondary endpoints included: time to 12- and 24-week confirmed disability progression (CDP); total number of T1 gadolinium-enhancing and new/enlarging T2 lesions at weeks 24, 48 and 96; and safety. Results Compared with IFNβ-1a, OCR reduced: ARR (OPERA I: 46%; OPERA II: 47%; both p<0.0001); 12- and 24-week CDP by 40% each (p=0.0006; p=0.0025, respectively) in pre-specified pooled analyses; T1 gadolinium-enhancing lesions (OPERA I: 94%; OPERA II: 95%; both p<0.0001); and new/enlarging T2 lesions (OPERA I: 77%; OPERA II: 83%; both p<0.0001). Except for infusion- and injection-related events, adverse events (AEs) and serious AEs were similar between groups. Conclusions OCR demonstrated significantly superior efficacy vs IFNβ-1a and a favourable safety profile in RMS patients.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".