P.026 Response to eculizumab in patients with myasthenia gravis recently treated with chronic intravenous immunoglobulin
Bibliographic record
Abstract
Background: Chronic intravenous immunoglobulin (IVIg) is used to treat refractory myasthenia gravis (MG). This subgroup analysis evaluated response to eculizumab in patients receiving chronic IVIg before entry to REGAIN, a phase 3, randomized, double-blind, placebo-controlled study of eculizumab in anti-acetylcholine receptor antibody-positive refractory generalized MG. Methods: IVIg was only permitted during REGAIN as rescue therapy; previously treated patients underwent a 4-week washout before randomization. Patients included in this analysis had received chronic IVIg ≥4 times in 1 year, with ≥1 dose within 6 months before REGAIN entry. Exacerbations and MG status changes were assessed. Results: Eighteen patients were evaluated; four experienced exacerbations (eculizumab-treated, 1/9; placebo-treated, 3/9). Clinically relevant improvements were larger with eculizumab than placebo, respectively (mean change, standard deviation [SD]: MG Activities of Daily Living score [MG-ADL], -5.3 [4.0] vs -2.1 [2.8]; Quantitative MG score [QMG], -4.1 [6.1] vs -1.3 [3.5]). More patients receiving eculizumab (7/9) had clinically meaningful responses (MG-ADL ≥3 and/or QMG ≥5 points) than those receiving placebo (3/9). Eculizumab safety was consistent with previous reports. Interim data from the open-label extension of REGAIN will be presented. Conclusions: In patients previously receiving chronic IVIg, eculizumab showed a trend toward meaningful clinical improvements and fewer exacerbations compared with placebo. (NCT01997229, NCT02301624).
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".