2634 Cyclical rozanolixizumab treatment in generalised myasthenia gravis (gMG): pooled analysis of MycarinG (Phase 3 study) and two open-label extension studies
Bibliographic record
Abstract
Objective To assess efficacy and safety of cyclical rozanolixizumab treatment, based on study MG0003 with the open-label extensions (OLEs) MG0004 (NCT04124965) and MG0007 (NCT04650854). Methods MG0004 was an OLE study of ≤52 weekly rozanolixizumab subcutaneous infusions. In MG0007, after an initial cycle, cycles were ‘symptom-driven’, administered on symptom worsening (at investigator’s discretion, e.g., MG-ADL increase ≥2/Quantitative Myasthenia Gravis [QMG] increase ≥3). Data were pooled for patients with ≥2 symptom-driven cycles across MycarinG, MG0004 (first 6 weeks) and MG0007 (interim data) (efficacy); ≥1 cycle across MycarinG (symptom-driven) and MG0007 (fixed/symptom-driven) (safety). Results 127 (64.8%) patients received ≥2 symptom-driven rozanolixizumab cycles (7mg/kg, n=69; 10mg/kg, n=58). Treatment response was consistent across cycles 1–6; Day 43 mean change from baseline (CFB) in MG-ADL score for all rozanolixizumab-treated patients: Cycle 1 (n=127): −3.7; Cycle 2 (n=127): −3.9; Cycle 3 (n=98): −3.4; Cycle 4 (n=75): −3.8; Cycle 5 (n=51): −3.9; Cycle 6 (n=32): −4.5. Day 43 mean CFB in QMG score was: Cycle 1 (n=127): −5.4; Cycle 2 (n=125): −4.7; Cycle 3 (n=97): −4.7; Cycle 4 (n=74): −5.1; Cycle 5 (n=51): −4.5; Cycle 6 (n=32): −6.3. Myasthenia Gravis Composite scale reductions were consistent across cycles. Patients with >1 year participation had a median of 4 cycles in the first year. Treatment-emergent adverse events (most mild-to-moderate) occurred in 77.4% and 91.6% of patients receiving ≥1 cycle of rozanolixizumab 7mg/kg and 10mg/kg, respectively. Conclusion Rozanolixizumab efficacy was maintained over up to 6 symptom-driven treatment cycles across multiple MG-specific endpoints with an acceptable safety profile. Funding: UCB Pharma.
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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 | 0.018 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.011 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".