P.043 Long-term safety, tolerability, and efficacy of efgartigimod in patients with Generalized Myasthenia Gravis: concluding analyses from ADAPT+
Bibliographic record
Abstract
Background: Efgartigimod is a human IgG1 antibody Fc-fragment that reduces total and pathogenic IgG autoantibody levels through FcRn blockade. ADAPT was a phase 3 trial evaluating efgartigimod in patients with generalized myasthenia gravis (gMG). Patients who completed ADAPT could enroll in ADAPT+ (open-label extension). Methods: Efgartigimod (10 mg/kg intravenous) was administered in cycles of 4 weekly infusions, with subsequent cycles initiated based on clinical evaluation. ADAPT+ evaluated long-term safety and tolerability of efgartigimod in patients with gMG. Efficacy was assessed utilizing MG-ADL and QMG scores. Results: Of 167 patients from ADAPT, 151 (90%) entered ADAPT+, and 145 received ≥1 cycle as of January 2022. Over 217.55 patient-years of follow-up (mean duration per patient, 548 days), incidence of adverse events did not increase with subsequent cycles. AChR-Ab+ patients with ≥1 year of follow-up across ADAPT/ADAPT+ (n=95) received a median (range) 5.0 (0.4–7.6) cycles per year. All AChR-Ab+ patients (n=111) demonstrated consistent improvements (mean change [SE], week 3 of cycle 1) in MG-ADL (-5.0 [0.33]; up to 14 cycles) and QMG (-4.7 [0.41]; up to 7 cycles) scores during each cycle. Conclusions: These ADAPT+ analyses suggest long-term efgartigimod treatment is well tolerated and efficacious. Additional final data cut analyses will be presented at CNSF 2023.
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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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".