Rituximab in "Resistant" Myasthenia Gravis (P3.177)
Bibliographic record
Abstract
Objective: To examine the role of rituximab in "resistant" myasthenia gravis (MG). Background: Myasthenia gravis is an autoimmune disease characterized by fatigable weakness. 80-85[percnt] of patients with MG respond favorably to standard treatments, which include steroids and other disease-altering agents. The other 15-20[percnt] have a sub-optimal response to available treatments and are defined here as treatment resistant. Rituximab, a novel anti-CD20 antibody, has been used extensively in a number of rheumatological and hematological diseases. Here we examine the role of rituximab in treatment-resistant MG. Methods: Rituximab is administered to treatment-resistant MG patients, according to standard protocols. Results: The primary outcome of the study is manual muscle testing (MMT) score, with the secondary outcomes being reduction in steroid dose and change in frequency of IVIG infusions or plasma exchanges. To date, 15 patients have been enrolled in the study. To date, MMT score has shown reduction from a baseline of 10.4 ± 2.5 to 3.0 ± 1.3 after rituximab infusion. The time to peak response is 4 ± 0.6 months. Average steroid dose has decreased from 22.8 ± 6.8 mg to 5.0 ± 1.7 mg. The frequency of IVIG infusions has decreased from 3.0 ± 1.0 to 0.2 ± 0.2, while the frequency of plasma exchanges has decreased from 2.2 ± 0.8 to 0.8 ± 0.8. Repeat infusions are also possible, demonstrating both safety and efficacy. To date, no adverse events have been detected. Conclusions: Rituximab is a safe and efficacious treatment for patients with treatment-resistant MG.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".