Improvement in Patient‐Reported Symptoms of Generalised Myasthenia Gravis With Rozanolixizumab in the Randomised Phase 3 MycarinG Study Using the MG Symptoms PRO
Bibliographic record
Abstract
BACKGROUND: In the Phase 3 MycarinG study (NCT03971422), rozanolixizumab improved myasthenia gravis (MG)-specific outcomes versus placebo in patients with generalised MG, including those measured by the five independent MG Symptoms patient-reported outcome (PRO) scales: Muscle Weakness Fatigability (MWF), Physical Fatigue (PF) and Bulbar Muscle Weakness (BMW) as secondary endpoints and Ocular Muscle Weakness and Respiratory Muscle Weakness (exploratory endpoints). This research aimed to provide further insights into these improvements. METHODS: Post hoc analyses evaluated correlation (Pearson coefficient) between MG Symptoms PRO and subdomain scores of MG Activities of Daily Living (MG-ADL) and Quantitative MG (QMG) at baseline. Proportions of responders reaching clinically meaningful thresholds and analyses at the item level (observed mean change and Rasch modelling of predicted change from baseline) are reported for MWF, PF, and BMW with rozanolixizumab versus placebo at Day 43. RESULTS: Correlation coefficients between MG Symptoms PRO and MG-ADL were strong (≥ 0.7) for ocular and bulbar scores and moderate (0.5 to < 0.7) for other scores. Correlations with clinician-assessed QMG scores were generally weak (< 0.5). For MWF, PF, and BMW, greater proportions of responders were observed with rozanolixizumab 7 mg/kg (46.9%, 31.3% and 26.6%, respectively) or 10 mg/kg (56.5%, 48.4% and 32.3%) versus placebo (28.1%, 26.6% and 10.9%). Item-level analyses demonstrated rozanolixizumab benefit at a symptom-specific level. DISCUSSION: MG Symptoms PRO scales correlate well with concepts in MG-ADL while assessing additional concepts, such as PF and MWF. Results from the MG Symptoms PRO in MycarinG reflected improvements from baseline in patient-relevant symptoms, including fatigue, with rozanolixizumab.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".