An evaluation of rozanolixizumab-noli for the treatment of anti-AChR and anti-MuSK antibody-positive generalized myasthenia gravis
Bibliographic record
Abstract
INTRODUCTION: Myasthenia gravis (MG) is an auto-immune disease characterized by fluctuating symptoms of muscle weakness and fatigue. Corticosteroids and corticosteroid-sparing broad-spectrum immunosuppression play a great role in the treatment of myasthenia gravis. However, debilitating side effects and long time to treatment effect highlight the need for development of novel target-specific medications. Rozanolixizumab is a highly specific neonatal Fc receptor (FcRn) inhibitor that acts on immunoglobulin G (IgG) homeostasis. Results from the MycarinG Phase III randomized controlled trial demonstrated significant efficacy of rozanolixizumab in generalized MG in terms of primary outcome and all secondary endpoints, tolerability, and safety compared to placebo. AREAS COVERED: We included different trials on myasthenia gravis and rozanolixizumab which include Phase II (NCT03052751) and Phase III MycarinG (NCT03971422) studies. EXPERT OPINION: Clinical trials have demonstrated that rozanolixizumab has strong efficacy with a 78% reduction in pathogenic IgG like plasma exchange (PLEX) and has therapeutic benefits comparable with PLEX and IVIG. It has less treatment adverse events and is easily accessible through subcutaneous infusion. The safety and effectiveness of rozanolixizumab need to be assessed further in the real-world context in post-marketing studies. If current trial information holds true, rozanolixizumab may become a medication of choice for MG in succeeding years.
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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 | 0.001 | 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".