Rozanolixizumab efficacy in generalised myasthenia gravis: Subgroup analyses from the randomised, phase 3, MycarinG study
Bibliographic record
Abstract
Background Establish efficacy of rozanolixizumab (FcRn inhibitor) in generalised myasthenia gravis (gMG) across subgroups. Design/Methods The Phase 3 MycarinG study (MG0003/NCT03971422) randomised adults (MGFA Class II–IVa AChR/MuSK autoantibody-positive gMG) to weekly rozanolixizumab 7mg/kg, 10mg/kg or placebo for 6 weeks. The primary endpoint was least squares mean (LSM) change from baseline (CFB) to Day 43 in MG-ADL. Results Patients (N=200) were randomised to rozanolixizumab 7mg/kg (n=66), 10mg/kg (n=67) or placebo (n=67). In the overall population, LSM MG-ADL CFB at Day 43 was −3.4, −3.4 and −0.8 for rozanolixizumab 7mg/kg, rozanolixizumab 10mg/kg and placebo, respectively. Mean observed MG-ADL CFB at Day 43 was more reduced in both rozanolixizumab groups than the placebo group across subgroups: ≥1 prior therapy (n=163): −3.2, −3.3 and −1.0, respectively; ≥2 prior therapies (n=84): −2.5, −3.0 and −0.8; baseline QMG ≤15 (n=110): −3.7, −2.6 and −0.6; baseline QMG >15 (n=90): −3.0, −4.0 and −0.7; baseline disease duration <4 years (n=78): −2.9, −3.1 and −0.6; baseline disease duration ≥4 years (n=122): −3.8, −3.3 and −0.7. TEAEs occurred in 81.3%, 82.6% and 67.2% of patients, respectively. Conclusion Rozanolixizumab treatment demonstrated greater reductions in MG-ADL score than placebo in a broad range of patients with gMG. Funding: UCB Pharma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.013 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".