Ocrelizumab reduces progression of upper extremity impairment in patients with primary progressive multiple sclerosis: Findings from the phase III randomized ORATORIO trial
Bibliographic record
Abstract
BACKGROUND: Upper extremity (UE) impairment is common with primary progressive multiple sclerosis (PPMS). OBJECTIVE: This exploratory analysis examined the effects of ocrelizumab on confirmed progression (CP) and confirmed improvement (CI) in UE impairment in patients from ORATORIO. METHODS: Patients with PPMS received ocrelizumab 600 mg or placebo every 24 weeks for ⩾120 weeks. The Nine-Hole Peg Test (9HPT) was administered at baseline (BL) and every 12 weeks thereafter. Prespecified exploratory endpoints included change in 9HPT time and proportion of patients with CP of ⩾20% in 9HPT. Analysis populations included intention-to-treat (ITT) patients and subgroups stratified by BL 9HPT time and Expanded Disability Status Scale. Post hoc analyses included the proportion of patients achieving more severe thresholds of CP and the proportion achieving CI in 9HPT. RESULTS: Among ITT patients, ocrelizumab significantly reduced the change in 9HPT time over 120 weeks, the risk of CP of ⩾20% in 9HPT time for both hands and the risk of more severe 9HPT progression versus placebo. Numerical trends also favoured ocrelizumab versus placebo with respect to achieving CI. Consistent directional trends were observed in subgroup analyses. CONCLUSION: Ocrelizumab reduces the risk of UE disability progression and may increase the possibility of improvement versus placebo in PPMS.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".