Bibliographic record
Abstract
BACKGROUND: In myasthenia gravis (MG), the prototypic autoimmune disease, antibodies against acetylcholine receptors impair neuromuscular transmission and produce weakness. Although recognized for several hundred years, it has only been over the last three decades that effective treatments have become available for MG. REVIEW SUMMARY: This review summarizes the principles of normal neuromuscular transmission, the clinical features of MG, and the tests available for its diagnosis. The current treatments for MG are discussed, including possible mechanisms of action and a discussion of potential adverse effects. When available, evidence-based justification for individual treatment options is given, and areas of controversy identified. CONCLUSIONS: Significant improvements in the diagnosis and management of MG have been made over the last several decades. The available treatments either improve neuromuscular transmission directly, or suppress or modulate the pathogenic immune response in MG. Treatment is highly individualized and must take into account the severity of disease, the presence of other diseases, and the kinetics of response for the available treatments. This requires detailed knowledge of the mechanisms of action and possible adverse effects for each treatment. However, despite an optimistic outlook with modern treatment, the management of MG continues to be plagued by lack of efficacy in some, and significant adverse effects in most MG patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| 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) | 0.007 | 0.002 |
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".