2999 Response to rozanolixizumab across treatment cycles in patients with generalised myasthenia gravis: a<i>post hoc</i>analysis
Bibliographic record
Abstract
Background In the Phase 3 MycarinG ( NCT03971422) study, one cycle (six once-weekly subcutaneous infusions) of rozanolixizumab 7mg/kg or 10mg/kg significantly improved myasthenia gravis (MG)-specific outcomes versus placebo. After MycarinG, patients could enrol in open-label extensions MG0004 (NCT04124965) or MG0007 (NCT04650854). We evaluated response to rozanolixizumab over multiple treatment cycles based on Cycle 1 (C1) response.Methods Data were pooled across MycarinG, MG0004 (first 6 weeks) and MG0007 (interim data cut-off: 08 July 2022) for patients with ≥2 symptom-driven cycles. Proportion of patients achieving MG-Activities of Daily Living (MG-ADL) and Quantitative MG (QMG) response (≥2.0-point and ≥3.0-point improvement from baseline, respectively) at Day 43 in each cycle was analysed. Post hoc analyses of response rates were conducted based on C1 response.Results 127 patients had ≥2 symptom-driven cycles. In C1, 74.0% (94/127) and 68.5% (87/127) of patients were MG-ADL and QMG responders, respectively, at Day 43. Among MG-ADL C1 responders, MG-ADL response rates remained high over subsequent cycles (C2: 78.7% [74/94]; C3: 77.1% [54/70]; C4: 78.0% [46/59]). Similar patterns were observed for QMG response. Of 33 (26.0%) MG-ADL non-responders at C1, 63.6% (21/33) were responders at C2. Of 40 (31.5%) QMG non-responders at C1, 51.3% (20/39) were responders at C2.Conclusions Patients receiving rozanolixizumab demonstrated a high response rate over multiple cycles, irrespective of initial response. Initial non-responders may benefit from additional rozanolixizumab treatment cycles.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".