Efficacy of Rituximab in Refractory Generalized anti-AChR Myasthenia Gravis
Bibliographic record
Abstract
BACKGROUND: Several retrospective case series have suggested rituximab (RTX) might improve patients with refractory Myasthenia Gravis (MG). OBJECTIVE: In this study, we aimed to evaluate prospectively the efficacy of RTX on muscle function in refractory generalized anti-acetylcholine receptor (AChR) MG patients. METHODS: Enrolled patients received 1 g of RTX at day 0, day 14, and 6-month follow-up (M6). The primary endpoint was improvement of muscle function at 12-month (M12) based on myasthenic muscle score (MMS). Secondary endpoints were an improvement of the MG Foundation of America Postintervention Status (MGFA-PIS), respiratory forced vital capacity, occurrences of acute MG exacerbation and requirement of associated immunosuppressants and immunomodulatory agents. RESULTS: Twelve patients were enrolled, and 11 completed the study. Only a single patient presented an improvement of at least 20 points on MMS at M12, although 2 patients displayed an increase of at least 18 points at M12. MGFA-PIS had improved in 55% of patients by M12. The clinical improvement was not associated with a reduction of immunosuppressant burden. CONCLUSIONS: These results provide data on the effect of RTX in patients with severe, refractory anti-AChR Abs generalized MG. Even though primary outcome was only reached in a single patient at M12, a beneficial effect of RTX on muscle function was seen in half of the patients at M12 and persisted in a third of patients at M18.
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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 | 0.001 | 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) | 0.000 | 0.000 |
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".