P.012 A global, long-term, prospective, observational registry of patients with AQP4+ NMOSD treated with complement component 5 inhibitor therapies eculizumab or ravulizumab
Bibliographic record
Abstract
Background: The complement component 5 inhibitor therapies (C5ITs) eculizumab and ravulizumab have been approved or submitted for regulatory approval in several regions for AQP4+ NMOSD. Methods: This global, long-term, prospective, multicenter, observational registry will enroll adult patients with AQP4+ NMOSD being treated with eculizumab or ravulizumab and who have received ≥1 dose of eculizumab or ravulizumab within 4 or 12 weeks prior to enrollment, respectively. Inclusion criteria include available historical data on C5IT dosing since initiation and the number and types of relapses from 1 year prior to C5IT initiation through enrollment. The primary outcome is annualized relapse rate. Safety outcomes will include serious adverse events, meningococcal infections, and pregnancy, breastfeeding, and neonatal outcomes. Data will be collected prospectively for up to 5 years. Approximately 130 patients will be enrolled, with a maximum of around 200 patients in up to 10 countries globally. Results: N/A Conclusions: This registry will collect data to characterize the long-term effectiveness and safety of the C5ITs eculizumab and ravulizumab in patients with AQP4+ NMOSD to provide evidence on the real-world impact of C5ITs in this patient population.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".