Data from: Randomized phase 2 study of FcRn antagonist efgartigimod in generalized myasthenia gravis
Bibliographic record
Abstract
Objective: To investigate safety and explore efficacy of efgartigimod (ARGX-113), an anti-neonatal Fc receptor immunoglobulin G1 Fc fragment, in patients with generalized myasthenia gravis (gMG) with a history of anti-acetylcholine receptor (AChR) autoantibodies, who were on stable standard-of-care myasthenia gravis (MG) treatment. Methods: A phase 2, exploratory, randomized, double-blind, placebo-controlled, 15-center study is described. Eligible patients were randomly assigned (1:1) to receive 4 doses over a 3-week period of either 10 mg/kg IV efgartigimod or matched placebo combined with their standard-of-care therapy. Primary endpoints were safety and tolerability. Secondary endpoints included efficacy (change from baseline to week 11 of Myasthenia Gravis Activities of Daily Living, Quantitative Myasthenia Gravis, and Myasthenia Gravis Composite disease severity scores, and of the revised 15-item Myasthenia Gravis Quality of Life scale), pharmacokinetics, pharmacodynamics, and immunogenicity. Results: Of the 35 screened patients, 24 were enrolled and randomized: 12 received efgartigimod and 12 placebo. Efgartigimod was well-tolerated in all patients, with no serious or severe adverse events reported, no relevant changes in vital signs or ECG findings observed, and no difference in adverse events between efgartigimod and placebo treatment. All patients treated with efgartigimod showed a rapid decrease in total immunoglobulin G (IgG) and anti-AChR autoantibody levels, and assessment using all 4 efficacy scales consistently demonstrated that 75% showed a rapid and long-lasting disease improvement. Conclusions: Efgartigimod was safe and well-tolerated. The correlation between reduction of levels of pathogenic IgG autoantibodies and disease improvement suggests that reducing pathogenic autoantibodies with efgartigimod may offer an innovative approach to treat 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".