P.103 Effectiveness and safety of ravulizumab in generalized Myasthenia Gravis (gMG): Updated analysis from a global registry
Bibliographic record
Abstract
Background: The complement C5 inhibitor (C5IT), ravulizumab, is approved in Canada for the treatment of anti-acetylcholine receptor antibody-positive (AChR-Ab+) generalized myasthenia gravis (gMG). Updated effectiveness and safety results from the ongoing MG SPOTLIGHT Registry (NCT04202341) are reported. Methods: MGFA classification and MG-ADL total scores were assessed in patients who received ravulizumab only (ravu-only) or transitioned from eculizumab to ravulizumab (ecu-to-ravu), with data available prior to C5IT initiation (“pre-C5IT”) and ≥1 assessment post-initiation (“post-ravu”). Results: Of 52 patients with 2 post-ravu assessments, average treatment duration was 10.4 months at last assessment (LA). Mean±SD MG-ADL scores improved (pre-C5IT: 7.6±3.6; LA: 3.4±3.3), as did the proportions of patients with minimal symptom expression (MSE, MG-ADL≤1) (pre-C5IT: 1/52 [2%]; LA: 17/52 [33%]) and MGFA classification 0-II (pre-C5IT: 18/45 [40%]; LA: 40/45 [89%]). In the ravu-only subgroup, outcomes improved (pre-C5IT vs LA): MG-ADL, 6.3±3.0 vs 4.0±3.4; MGFA 0-II, 9/14 [64%] vs 12/14 [86%]. The ecu-to-ravu subgroup sustained continued gradual improvement from last eculizumab assessment to LA: MG-ADL, 4.4±4.2 vs 3.0±2.8; MGFA 0-II, 19/21 [90%] vs 20/21 [95%]. Ravulizumab was well tolerated; no meningococcal infections were reported. Conclusions: These results demonstrate the long-term effectiveness and safety of ravulizumab in routine clinical practice in patients with gMG.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".