Baseline Demographics and Disease Characteristics from OPERA I and II, Two Phase III Trials Evaluating Ocrelizumab in Patients with Relapsing Multiple Sclerosis (P7.201)
Bibliographic record
Abstract
OBJECTIVE: To present demographic and baseline disease characteristics of patients in the OPERA I and II studies. BACKGROUND: Ocrelizumab (OCR) is a recombinant humanized monoclonal antibody that selectively targets CD20+ B-cells. Two Phase III trials, OPERA I and II, are investigating the efficacy and safety of OCR compared with INFβ in patients with relapsing MS (RMS). DESIGN/METHODS: The OPERA trials are randomized, double-blinded, double-dummy, parallel-group studies investigating the efficacy and safety of 600 mg OCR administered by intravenous infusion every 24 weeks compared with high-dose, high-frequency IFNβ1a (44 µg 3x per week). Patients were randomized (1:1) to OCR or IFNβ. Entry criteria included a diagnosis of RMS (McDonald criteria, 2010), Expanded Disability Status Scale (EDSS) score of 0-5.5, and age of 18-55 years. At least 2 documented relapses within the last 2 years or one relapse in the last one year prior to screening were required. The primary endpoint is the annualized relapse rate at 2 years. RESULTS: A total of 821 and 835 patients were randomized in OPERA I and II, respectively. Mean baseline age was 37.0 and 37.3 years, respectively in OPERA I and II; 66.0[percnt] of patients were female. Patients had symptoms of MS for a mean duration of 6.5 years in OPERA I and 6.7 years in OPERA II. The mean EDSS score was 2.80 in OPERA I and 2.82 in OPERA II. The mean number of gadolinium enhancing (Gd+) lesions on brain MRI was 1.79, with 59.7[percnt] of patients with no Gd+ lesions in OPERA I, and 1.87 and 59.8[percnt] respectively in OPERA II. CONCLUSIONS: The OPERA I and II baseline data are consistent with a RMS population. The results of the OPERA studies will provide information on the efficacy and safety of ocrelizumab compared with IFNβ in RMS. Study Supported by: F.Hoffmann-La Roche
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".