<scp>ADAPT NXT</scp>: Fixed Cycles or Every‐Other‐Week <scp>IV</scp> Efgartigimod in Generalized Myasthenia Gravis
Bibliographic record
Abstract
OBJECTIVE: This phase 3b, open-label, randomized ADAPT NXT study investigated the efficacy, safety, and tolerability of efgartigimod administered in either a fixed cycles dosing regimen (3 cycles of 4 once-weekly infusions, with 4 weeks between cycles) or a cycle followed by every-other-week (Q2W) dosing. METHODS: Adult participants with anti-acetylcholine receptor antibody-positive generalized myasthenia gravis (gMG) were randomized 3:1 to Q2W or fixed cycles dosing of efgartigimod (10 mg/kg intravenously) for 21 weeks. The primary endpoint was the mean change from baseline in total Myasthenia Gravis Activities of Daily Living (MG-ADL) score averaged across 21 weeks. RESULTS: Sixty-nine participants were treated (fixed cycles, n = 17; Q2W, n = 52). Least squares (LS) mean (95% CI) of the change from baseline in MG-ADL total score from Weeks 1 to 21 was -5.1 (-6.5 to -3.8) in the fixed cycles arm and -4.6 (-5.4 to -3.8) in the Q2W arm. Clinical improvements were observed in MG-ADL total scores as early as Week 1 and were maintained throughout the study. Achievement of minimal symptom expression (MG-ADL: 0-1) from Weeks 1 to 21 occurred in 47.1% (n = 8/17) and 44.2% (n = 23/52) of participants in the fixed cycles and Q2W arms, respectively. Efgartigimod was well tolerated; COVID-19, headache, and upper respiratory tract infection were the most common treatment-emergent adverse events. INTERPRETATION: Efgartigimod administered as either fixed cycles or Q2W dosing results in rapid, robust, and sustained clinically meaningful improvement. These results build upon previous studies and provide additional efgartigimod dosing approaches to achieve and sustain clinical efficacy in patients with gMG.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".