Short- and long-term effects of early versus delayed treatment with ocrelizumab on cerebellar volume loss in patients with RMS and PPMS
Bibliographic record
Abstract
BACKGROUND: The cerebellum is a functionally and anatomically complex structure, which, in multiple sclerosis (MS), is affected by focal white/gray matter lesions and by secondary neurodegeneration of afferent/efferent connections to the supratentorial brain and the spinal cord. OBJECTIVES: To assess the efficacy of ocrelizumab compared with interferon β-1a (IFN β-1a)/placebo on cerebellar volume loss and the effect of switching to ocrelizumab on volume change in the Phase III trials in relapsing MS (RMS, OPERA I/II) and in primary progressive MS (PPMS, ORATORIO). METHODS: Cerebellar volume change was computed using paired Jacobian integration and analyzed using a mixed-effect repeated measurement model. RESULTS: In RMS, ocrelizumab reduced cerebellar volume loss in the double-blind period (DBP) and the difference (30% at DBP end) was maintained in the open-label extension (OLE) after control patients (IFN β-a) were switched to ocrelizumab. In PPMS, there was a small numerical difference in the DBP, but a larger (up to 22%) difference in favor of ocrelizumab in the OLE. CONCLUSIONS: In both RMS and PPMS, early treatment with ocrelizumab helps to prevent additional cerebellar volume loss compared with delayed switching to ocrelizumab. Further analysis is needed to fully understand the clinical impact of cerebellar atrophy.
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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 | 0.002 |
| 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".