Immunopathology of Autoimmune Myasthenia Gravis: Implications for Improved Testing Algorithms and Treatment Strategies
Bibliographic record
Abstract
Myasthenia gravis (MG) is a heterogeneous condition, characterized by autoantibodies (Abs) that target functionally important structures within neuromuscular junctions (NMJ), thus affecting nerve-to-muscle transmission. MG patients are more often now subgrouped based on the profile of serum autoantibodies, which segregate with clinical presentation, immunopathology, and their response to therapies. The serological testing plays an essential role in confirming MG diagnosis and guiding disease management, although a small percentage of MG patients remain negative for antibodies. With the advancements in new highly effective pathophysiologically-specific immunotherapeutic options, it has become increasingly important to identify the specific Abs responsible for the pathogenicity in individual MG patients. There are several new assays and protocols being developed for the improved detection of Abs in MG patients. This review focuses on the divergent immunopathological mechanisms in MG, and discusses their relevance to improved diagnostic and treatment. We propose a comprehensive "reflex testing," algorithm for the presence of MG autoantibodies, and foresee that in the near future, the convenience and specificity of novel assays will permit the clinicians to consider them into routine systematic testing, thus stimulating laboratories to make these tests available. Moreover, adopting treatment driven testing algorithms will be crucial to identify subgroups of patients potentially benefiting from novel immunotherapies for MG.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 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) | 0.000 | 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 teacher head, 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".