Efficacy and safety of rituximab in the treatment of refractory myasthenia gravis: Meta-analysis
Bibliographic record
Abstract
Objective To evaluate the efficacy and safety of rituximab (RTX) in the treatment of refractory myasthenia gravis (MG). Methods Retrieve relevant retrospective case analysis and observational studies that reported RTX therapy in patients with refractory MG from online databases (January 1, 2000-April 30, 2018) in PubMed, EMBASE/SCOPUS and Cochrane Online Library with key words: rituximab, myasthenia gravis. Quality of studies was evaluated by using Newcastle-Ottawa Scale (NOS). All data were pooled by Stata 12.0 software for Meta-analysis. Results A total of 1772 articles were enrolled, and 10 retrospective case analysis and one observational study with 160 eligible participants taking RTX were finally included after excluding duplicates and those not meeting the inclusion criteria. Among 160 patients, 88 were tested positive for acetylcholine receptor antibody (AChR- Ab+), 65 were tested positive for muscle-specific receptor tyrosine kinase antibody (MuSK-Ab+ ), and 7 were tested negative for two antibodies. Meta-analysis showed the overall effective rate of RTX treating refractory MG patients with serum AChR-Ab+ was 77% (95%CI: 0.642-0.899, P = 0.030); the overall effective rate of RTX treating refractory MG patients with serum MuSK-Ab+ was 73% (95%CI: 0.631-0.829, P = 0.048); RTX significantly reduced average daily dose of corticosteroids [before treatment 31.81 (20.00, 45.90) mg/d, after treatment 6.81 (3.44, 8.00) mg/d, decrement 21.70 (15.50, 42.46) mg/d; Z = 2.366, P = 0.018]; the main side effects of RTX were leukopenia, paroxysmal atrial fibrillation, infectious pneumonia, progressive multifocal leukoencephalopathy (PML), oral herpes zoster, et al. Conclusions Critical findings have been demonstrated in the evidence-based evaluation on efficacy and safety of RTX in the treatment of refractory MG. The effect of RTX on refractory MG is remarkable, and can significantly reduce the average daily dosage of corticosteroids. DOI: 10.3969/j.issn.1672-6731.2018.07.005
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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 | 0.021 | 0.037 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.067 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".