P.056 Real-world reduction in oral corticosteroid utilization following efgartigimod initiation in patients with generalized myasthenia gravis
Bibliographic record
Abstract
Background: Reducing oral corticosteroids (OCS) use can alleviate the risk of many adverse events related to long-term OCS use. Here, we evaluate real-world utilization of OCS among patients with generalized myasthenia gravis (gMG) over the first 6 months following efgartigimod initiation. Methods: Patients with gMG using OCS who initiated efgartigimod treatment were identified retrospectively from an open US medical and pharmacy claims database (IQVIA Longitudinal Access and Adjudication Data [LAAD], April 2016-April 2023). Average daily dose (ADD) of OCS was analyzed during the 3-month period preceding efgartigimod initiation, and at 3 and 6 months post-efgartigimod initiation. Results: Of 231 patients assessed, 17 (7.4%), 109 (47.1%), and 105 (45.5%) had baseline OCS ADD of 0–5 mg, 5–20 mg, or >20 mg, respectively. At 3 and 6 months post-efgartigimod, 82 (35%) and 99 (43%) patients, respectively, reduced ADD by ≥5 mg. Proportion of patients with ADD of 0–5 mg increased >3-fold (7% baseline vs. 26% 6 months post-efgartigimod) and proportion of patients with ADD of >20 mg decreased by 35% (45% baseline vs. 29% 6 months post-efgartigimod) following efgartigimod initiation. Conclusions: Approximately 43% of patients were able to decrease steroid use or achieved steroid-free status within 6 months of efgartigimod treatment initiation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".