P.034 Minimal symptom expression following treatment with efgartigimod in patients with Generalized Myasthenia Gravis
Bibliographic record
Abstract
Background: Efgartigimod is a human IgG1 antibody Fc-fragment that reduces IgG levels through FcRn blockade. A key efficacy indicator in the treatment of IgG autoantibody-mediated generalized myasthenia gravis (gMG) is improvement in MG-ADL score. Methods: The ADAPT phase 3 trial evaluated safety and efficacy of efgartigimod in patients with gMG, including reaching and maintaining of minimal symptom expression (MSE; defined as an MG-ADL total score of 0 or 1). Results: 167 patients (AChR-Ab+, n=129; AChR-Ab-, n=38) were randomized to receive treatment cycles of 4 weekly infusions of efgartigimod or placebo. Significantly more AChR-Ab+ efgartigimod-treated patients achieved MSE during cycle 1 compared to placebo-treated patients (40.0% [n=26/65] vs 11.1% [n=7/63; P<0.0001]). In cycle 2, 31.4% (n=16/51) of AChR-Ab+ patients in the efgartigimod cohort achieved MSE compared to none in the placebo cohort. MG-ADL score improved by ≥6 points in 56.9% of AChR-Ab+ efgartigimod-treated patients compared to 20.6% of placebo-treated patients in cycle 1. Most patients achieved MSE by week 4 of a cycle, paralleling early reduction in IgG levels, and MSE duration ranged from 1 to ≥10 weeks. Adverse events were predominantly mild to moderate. Conclusions: Efgartigimod treatment resulted in more patients with AChR-Ab+ gMG achieving both MSE and clinically meaningful MG-ADL improvements.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".