177 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 patients with multiple sclerosis (MS). The safety/efficacy of ocrelizumab have been characterised in Phase II ( NCT00676715 ) and Phase III ( NCT01247324 ; NCT01412333 ; NCT01194570 ) trials in patients with relapsing-remitting MS, relapsing MS (RMS) and primary progressive MS (PPMS). We report ongoing safety evaluations from ocrelizumab clinical trials and open-label extension periods up to February 2018. Methods Safety outcomes were reported for the ocrelizumab all-exposure population in Phase II/III and ongoing Phase IIIb MS trials. The number of post-marketing ocrelizumab-treated patients is based on estimated number of vials sold and US claims data. To account for different exposure lengths, rates per 100 patient years (PY) are presented. Results In clinical trials, 3,811 patients with MS received ocrelizumab (10,919 PY of exposure, as of February 2018). Reported rates per 100 PY (95% confidence interval) were: adverse events (AEs), 242 (239–245); serious AEs, 7.23 (6.73–7.75); infections, 74.5 (72.9–76.1); serious infections, 2.00 (1.74–2.28); and malignancy 0.45 (0.33–0.60). Updated post-marketing data will be presented. Conclusions Reported rates of events in the ocrelizumab all-exposure population continue to be generally consistent with the controlled treatment period in RMS/PPMS populations. Regular reporting of long-term safety data will continue. Disclosures Sponsored by F. Hoffmann-La Roche Ltd; writing and editorial assistance was provided by Articulate Science, UK, and funded by F. Hoffmann-La Roche Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 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".