178 Long-term efficacy of ocrelizumab in relapsing multiple sclerosis
Bibliographic record
Abstract
Background The efficacy and safety of ocrelizumab in relapsing multiple sclerosis were demonstrated in the double-blind control period of the Phase III OPERA I/II trials ( NCT01247324 /NCT01412333). Here we assessed the efficacy of switching to or maintaining ocrelizumab therapy after 3 years’ follow-up in the open-label extensions (OLEs) of these studies. Methods At OLE commencement, patients continued ocrelizumab (OCR-OCR) or switched from interferon-β-1a (IFN) to ocrelizumab (IFN-OCR). Adjusted annualised relapse rate (ARR) and time-to-onset of 24-week confirmed disability progression (CDP24) were analysed. Results Among IFN-OCR patients, ARR decreased from 0.20 in the year pre-switch to 0.10, 0.08 and 0.07 at Years 1, 2 and 3 post-switch. OCR-OCR continuers maintained low ARRs through the year pre-OLE and the 3 years of OLE (0.13, 0.11, 0.08, 0.07). CDP24 was less frequent in OCR-OCR continuers versus IFN-OCR switchers in the year pre-switch and Years 1, 2 and 3 of OLE (7.7%/12.0%, 10.1%/15.6%, 13.9%/18.1% and 16.1%/21.3%; p<0.05, all comparisons). Conclusions Switching from IFN to ocrelizumab at the start of the OLE provided rapid reductions in ARR, maintained throughout the 3-year follow-up. After 5 years’ follow-up, patients who initiated ocrelizumab 2 years earlier accrued significant, sustained reductions in disability progression compared with patients switching from IFN. Disclosures Sponsored by F. Hoffmann-La Roche Ltd; writing and editorial assistance was provided by Articulate Science, UK, and funded by F. Hoffmann-La Roche Ltd.
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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 | 0.002 |
| 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 | 0.000 | 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".