Practice patterns in the management of myasthenia gravis: a cross-sectional survey of neurologists in the United States
Bibliographic record
Abstract
Background: Management of myasthenia gravis (MG), a rare immunoglobulin G autoantibody–mediated neuromuscular junction disorder, is driven by physician experience. To gain insight into current practices and physician needs, neurologists’ use of guidelines and disease activity evaluations to manage MG was assessed. Methods: In November and December of 2020, a quantitative, cross-sectional, 51-item, online survey–based study was used to collect data from 100 community neurologists, from 31 US states, who treat MG. Differences across ratio variables were analyzed via Chi-square and t tests, at a significance level of P<0.05. Results: Of respondents, 76% reported using clinical judgment rather than guidelines to inform treatment decisions, and only 29% reported awareness of the updated 2020 International Consensus Guidance for Management of Myasthenia Gravis. Treatment patterns reported include use of prednisone-equivalent corticosteroid doses ≤10 mg/day for ≥6 months (76% of respondents). When corticosteroids are contraindicated or after failure of an initial nonsteroidal immunosuppressant therapy (NSIST), immunoglobulin therapy is the respondents’ preferred initial treatment in patients with acetylcholine receptor antibody–positive generalized MG (vs a second NSIST). Respondents expressed interest in more guidance on crisis management, initiating/titrating maintenance medications, and managing patients with comorbidities. Conclusions: Respondents to this survey reported varied approaches to MG management and, in some clinical settings, heavier reliance on clinical judgment than on available consensus-based guidance. Also observed was potential underutilization of NSISTs in patients for whom corticosteroids are contraindicated, with reliance, instead, on immunoglobulin.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".