Myasthenia Gravis in Patients Treated With Immune Checkpoint Inhibitors
Bibliographic record
Abstract
Background: Immune checkpoint inhibitors (ICIs) have improved outcomes significantly for patients across multiple tumor types, and now are being used in combination with other therapies and in earlier settings where treatment intent is curative. Immune-related adverse events occur commonly and there are clear guidelines regarding management. Neurological toxicities such as myasthenia gravis (MG) with or without myositis are rare but are associated with high morbidity and mortality. Methods: This single-centre study presents a series of patients treated with ICIs who subsequently developed immune-related MG. Presenting symptoms, treatments and outcomes were abstracted from retrospective chart review. Results: We identified 16 patients (9 thoracic malignancies, 7 other tumor sites) who were diagnosed with MG after one or more cycles of ICI. Eleven had overlapping myositis. The median time from the first ICI treatment to the onset of symptoms was 49 days (range 17-361). All patients received steroids (prednisone 1-2 mg/kg); six required other immunosuppressive agents, and five underwent plasma exchange. Only two patients had complete resolution, eight improved with residual symptoms, two experienced initial improvement followed by deterioration, and four worsened despite treatment. Six patients died as a result of myasthenia-related complications (38%), three from progressive cancer (19%) and seven remain alive at the time of review (44%). Conclusion: ICI-related MG is a rare and potentially fatal adverse event. Diagnosis and management remain a challenge, especially with negative serological markers and in the presence of overlapping syndromes with high mortality rates. Prompt recognition and multimodality treatment are key. Clinicians should have a low threshold for diagnosis and early management.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".