034 Updated safety analysis of ocrelizumab in multiple sclerosis
Bibliographic record
Abstract
Background Ongoing safety reporting is crucial to understanding the long-term benefit-risk profile of ocrelizumab in multiple sclerosis (MS). Safety/efficacy of ocrelizumab have been characterised in Phase II ( NCT00676715 ) and III ( NCT01247324 /NCT01412333/ NCT01194570 ) trials in relapsing-remitting MS, relapsing MS (RMS) and primary progressive MS (PPMS). Here, we report safety evaluations from ocrelizumab clinical trials and open-label extensions up to January 2019, and selected post-marketing data. Methods Safety outcomes are reported for the ocrelizumab all-exposure population in Phase II/III and ongoing Phase IIIb trials. To account for different exposure lengths, rates per 100 patient years (PY) are presented. Results In clinical trials, 4,611 patients with MS received ocrelizumab (14,329 PY exposure). Reported rates per 100 PY (95% confidence interval) were: adverse events (AEs), 252 (249–254); serious AEs, 7.33 (6.89–7.79); infections, 76.7 (75.3–78.2); serious infections, 1.99 (1.77–2.23); malignancies, 0.46 (0.35–0.58); and AEs leading to discontinuation, 1.08 (0.92–1.27). Updated ocrelizumab all-exposure population data and selected post-marketing data will be presented. Conclusions Reported event rates in the ocrelizumab all-exposure clinical trial population and post-mar- keting settings remain generally consistent with the controlled treatment period in RMS/PPMS populations. Regular reporting of long-term safety data will continue. k.schmierer@qmul.ac.uk
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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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".