Immune Checkpoint Inhibitor–Induced Ocular Myasthenia Gravis: Case Series and Literature Review
Bibliographic record
Abstract
BACKGROUND: Immune checkpoint inhibitors (ICIs) have transformed cancer management by enhancing the immune system's ability to target cancer cells. However, they are known to be associated with immune-related adverse events, including ICI-induced myasthenia gravis (ICI-MG). There is limited literature on the isolated ocular form, ICI-induced ocular myasthenia gravis (ICI-OMG). The purpose of this study was to characterize clinical presentation and response to treatment of patients with ICI-OMG. METHODS: This case series and literature review focuses on isolated ICI-OMG. Data collected from patients at 2 North American academic neuro-ophthalmology practices included demographics, ICI type and duration of use, malignancy type, metastasis, symptoms, examination results, and treatment responses. A comprehensive literature review on isolated ICI-OMG cases was also conducted. RESULTS: Our data identified 3 patients with ICI-OMG with bilateral ptosis and diplopia, but no systemic or bulbar involvement at presentation. Literature review found 12 additional cases, with pembrolizumab being the most common ICI associated with OMG. All cases had ptosis, and most had diplopia. Testing for multiple OMG-associated antibodies was often negative and single-fiber electromyography (sfEMG) results varied. Treatments with corticosteroids, intravenous immunoglobulins, plasmapheresis, and pyridostigmine had mixed results. CONCLUSIONS: ICI-OMG is a rare and distinct condition, presenting significant diagnostic challenges due to often negative antibody tests and variable sfEMG results. Early recognition of ICI-OMG in patients with ptosis and/or diplopia is crucial. Treatment responses vary, and most patients do not return to baseline, indicating ICI-OMG is likely chronic. Further research is needed to improve diagnostic markers and treatment strategies.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".