THUR 179 Ocrelizumab effect on neda in patient subgroups of opera I and opera II
Bibliographic record
Abstract
Background In a pooled analysis of patients with relapsing multiple sclerosis in the Phase III OPERA I and OPERA II ( NCT01247324 /NCT014112333) studies, ocrelizumab increased the proportion of patients with no evidence of disease activity (NEDA) vs interferon β−1a (IFNβ1a); post hoc subgroup analyses are reported here. Methods NEDA (no 12 week confirmed disability progression, relapse, new/enlarging T2 lesions or T1 gadolinium enhancing lesions) rates were compared by Cochran–Mantel–Haenszel test on the pooled modified intent-to-treat (mITT) population (ocrelizumab, n=761 [600 mg intravenously every 24 weeks]; IFNβ1a, n=759 [44 μg subcutaneously three times weekly]; excludes patients with NEDA discontinuing for reasons unrelated to efficacy). Results Treatment benefit for NEDA in the mITT population with ocrelizumab vs IFNβ1a (47.7% vs 27.1%; p<0.001) was maintained across subgroups (ocrelizumab/IFNβ1a; p<0.001 unless stated): age (<40 years: 44.3%/22.6%; ≥40 years: 52.8%/33.7%), gender (male: 44.0%/22.2%; female: 49.7%/29.6%), prior [last 2 years] disease-modifying therapy (yes: 42.8%/23.9%; no: 49.5%/28.3%), prior relapses [last 12 months] (≤1: 49.2%/29.2%; ≥2: 44.2%/22.6%), baseline T1 Gd-enhancing lesions (none: 59.6%/38.8%; ≥1: 30.1%/10.2%) and baseline EDSS score (EDSS <2.5/<4.0: ocrelizumab 50.5%/50.3%, IFNβ1a 27.5%/26.4%; EDSS ≥2.5/≥4.0: ocrelizumab 46.0%/39.6%, IFNβ1a 26.9%/29.4% [NB: EDSS score ≥4: ocrelizumab/IFNβ1a p=0.043]). Conclusions Subgroup analyses were consistent with those of the overall pooled population on maintaining NEDA status.
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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 | 0.007 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".