Subcutaneous immunoglobulin in myasthenia gravis exacerbation
Bibliographic record
Abstract
Objective: To investigate the efficacy, tolerability, and safety of subcutaneous immunoglobulin (SCIg) in patients with mild to moderate myasthenia gravis (MG) exacerbation. Methods: We performed a prospective, open-label, phase 3 trial in patients with MG aged 18 years or older and mild to moderate worsening (transition from Myasthenia Gravis Foundation of America class I to II/III or class II to III), treated with SCIg (2 g/kg), self-administered over 4 weeks. The primary endpoint was change in quantitative MG (QMG) score from baseline to study end at 6 weeks. Secondary endpoints included change in manual muscle testing (MMT), MG activities of daily living (MG-ADL), and MG composite (MGC) scores, as well as occurrence of adverse events, and tolerability as assessed via Treatment Satisfaction Questionnaire for Medication (TSQM). Results: Twenty-two of 23 patients completed the study. QMG score decreased from 14.9 ± 4.1 to 9.8 ± 5.6 (p < 0.0001), MMT score decreased from 16.8 ± 9.5 to 5.2 ± 4.5 (p < 0.0001), MG-ADL score decreased from 9.5 ± 3.0 to 4.6 ± 3.0 (p < 0.0001), and MGC score decreased from 17.4 ± 5.0 to 5.6 ± 4.5 (p < 0.0001). Satisfaction by TSQM was high (79.6 ± 15.6%). Common adverse events included headache and injection site reactions. No serious adverse events occurred. Conclusions: SCIg is well-tolerated, safe, and effective in mild to moderate MG exacerbation. Comparative safety and efficacy must be established with randomized controlled trials. Classification of evidence: This study provides Class IV evidence that in patients with mild to moderate MG exacerbation, SCIg is safe and effective in reducing MG disability measures.
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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 | 0.001 |
| 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 | 0.001 |
| 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".