D.6 Changes in intravenous or subcutaneous immunoglobulin usage before and after efgartigimod initiation in patients with Myasthenia Gravis
Bibliographic record
Abstract
Background: While efgartigimod usage is expected to reduce immunoglobulin (IG) utilization, evidence in clinical practice is limited. Methods: In this retrospective cohort study, patients with gMG treated with efgartigimod for ≥1-year were identified from US medical/pharmacy claims data (April 2016-January 2024) and data from the My VYVGART Path patient support program (PSP). The number of IG courses during 1-year before and after efgartigimod initiation (index date) were evaluated. Patients with ≥6 annual IG courses were considered chronic IG users. Myasthenia Gravis Activities of Daily Living (MG-ADL) scores before and after index were obtained from the PSP where available. Descriptive statistics were used without adjustment for covariates. Results: 167 patients with ≥1 IG claim before index were included. Prior to efgartigimod initiation, the majority of patients (62%) received IG chronically. During the 1-year after index, the number of IG courses fell by 95% (pre: 1531, post: 75). 89% (n=149/167) of patients fully discontinued IG usage. Mean (SD) best-follow up MG-ADL scores were significantly reduced after index (8.0 [4.1] to 2.8 [2.1], P<0.05, n=73/167, 44%). Conclusions: Based on US claims, IG utilization was substantially reduced among patients who continued efgartigimod for ≥1-year, with patients demonstrating a favorable MG-ADL response.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".