P.023 Eculizumab shows consistent improvements across muscle groups in patients with AChR antibody-positive refractory myasthenia gravis
Bibliographic record
Abstract
Background: The physician-reported Quantitative Myasthenia Gravis (QMG) test was a key efficacy measure in REGAIN, a 26-week, phase 3, placebo-controlled study of eculizumab in anti-acetylcholine receptor antibody-positive refractory generalized MG. Ocular and generalized weakness have shown variable responses to therapies including prednisone and intravenous immunoglobulin/plasma exchange. Using the patient-reported MG Activities of Daily Living (MG-ADL) scale during REGAIN, eculizumab showed a consistent trend toward rapid and sustained improvement across bulbar, respiratory, limb and ocular domains. We analyzed the effect of eculizumab on bulbar, respiratory, gross motor and ocular domains during REGAIN, using the QMG test. Methods: QMG domain score changes to REGAIN week 26 were determined for patients with abnormal baseline scores. Repeated-measures analyses were performed for bulbar (swallowing/speech), respiratory (forced vital capacity), gross motor (limb/axial motor items) and ocular (ocular/facial muscles) domains. Results: Eculizumab-treated patients showed improvements in all four QMG domain scores to week 26. Rapid, sustained improvements were demonstrated across all domains, with a trend toward significant differences between eculizumab and placebo (bulbar, p=0.0628; respiratory, p=0.0682; gross motor, p=0.0114; ocular, p=0.0017). The eculizumab safety profile was consistent with previous reports. Conclusions: Eculizumab demonstrated a consistent response across all QMG muscle domains. This aligns with previously reported MG-ADL findings with eculizumab. (NCT01997229).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".