The efficacy and safety of efgartigimod for refractory myasthenia gravis: a systematic review and meta-analysis
Bibliographic record
Abstract
Myasthenia gravis (MG) is a chronic autoimmune disorder affecting the neuromuscular junction, where autoreactive immunoglobulin G (IgG) plays a key role in disease pathogenesis. The novel biologic Efgartigimod is a neonatal Fc receptor (FcRn) antagonist, promotes the lysosomal degradation of IgG, and may offer a targeted approach for managing MG. Despite the growing interest in efgartigimod, there remains a lack of comprehensive evaluation of its efficacy and safety in different MG subtypes. Comprehensive retrieval and screening were conducted on Pubmed, Embase, Web of Science, and Cochrane library to search studies on efgartigimod treatment. The data on response rates and adverse events were extracted, and the pooled effect size (ES) with the 95% confidence interval (CI) was calculated by fixed or random effect models. Sensitivity analysis and subgroup analysis were employed to test the heterogeneity. Funnel plots and trim-and-fill methods were used to test for publication bias. Data from 10 studies involving 305 patients were analyzed. The overall treatment response rate was 78% (95% CI: 67%–87%, I2 = 73.4%). Subgroup analysis revealed pooled response rates of 79.2% (95% CI: 68.5%–88.4%, I2 = 25.08%) in acetylcholine receptor antibody-positive MG (AChR+MG) patients and 76.2% (95% CI: 56.8%–91.5%, I2 = 85.95%) in group that did not differentiate auto-antibody types. The pooled incidence of adverse events was 38% (95% CI: 17%–51%, I2 = 92.59%), with infections (7%, 95% CI: 2%–14%, I2 = 62.5%), headache (7%, 95% CI: 1%–18%, I2 = 82.69%) and other (16%, 95% CI: 7%–28%, I2 = 71.81%). Among them, grade 3–4 adverse events are 1% (95% CI: 0%–2%, I2 = 0%). Our study demonstrates that efgartigimod is highly effective and well-tolerated in patients with refractory MG. These findings suggest that efgartigimod is a promising drug for the treatment of MG.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.052 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".