Efficacy and Safety of Rozanolixizumab in Moderate to Severe Generalized Myasthenia Gravis
Bibliographic record
Abstract
OBJECTIVE: To explore the clinical efficacy and safety of subcutaneous (SC) rozanolixizumab, an anti-neonatal Fc receptor humanized monoclonal antibody, in patients with generalized myasthenia gravis (gMG). METHODS: In this phase 2a, randomized, double-blind, placebo-controlled, 2-period, multicenter trial (NCT03052751), patients were randomized (1:1) in period 1 (days 1-29) to 3 once-weekly (Q1W) SC infusions of rozanolixizumab 7 mg/kg or placebo. In period 2 (days 29-43), patients were re-randomized to either rozanolixizumab 7 mg/kg or 4 mg/kg (3 Q1W SC infusions), followed by an observation period (days 44-99). Primary endpoint was change from baseline to day 29 in Quantitative Myasthenia Gravis (QMG) score. Secondary endpoints were change from baseline to day 29 in MG-Activities of Daily Living (MG-ADL) and MG-Composite (MGC) scores and safety. RESULTS: = 0.221; not statistically significant), MG-ADL (LS mean -1.8 vs -0.4, difference -1.4, 95% UCL -0.4), and MGC (LS mean -3.1 vs -1.2, difference -1.8, 95% UCL 0.4) scores. Efficacy measures continued to improve with rozanolixizumab 7 mg/kg in period 2. The most common adverse event in period 1 was headache (rozanolixizumab 57%, placebo 14%). CONCLUSION: Whereas change from baseline in QMG was not statistically significant, the data overall suggest rozanolixizumab may provide clinical benefit in patients with gMG and was generally well tolerated. Phase 3 evaluation is ongoing (NCT03971422). CLASSIFICATION OF EVIDENCE: This study provides Class I evidence that for patients with gMG, rozanolixizumab is well-tolerated, but did not significantly improve QMG score.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".