<p>Neuro-ophthalmic Complications of Immune Checkpoint Inhibitors: A Systematic Review</p>
Bibliographic record
Abstract
OBJECTIVE: Immune checkpoint inhibitors (ICIs) are novel cancer therapies that may be associated with immune-related adverse events (IRAEs) and come to the attention of neuro-ophthalmologists. This systematic review aims to synthesize the reported ICI-associated IRAEs relevant to neuro-ophthalmologists to help in the diagnosis and management of these conditions. METHODS: A systematic review of the literature indexed by MEDLINE, Embase, CENTRAL, and Web of Science databases was searched from inception to May 2020. Reporting followed the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) guidelines. Primary studies on ICIs and neuro-ophthalmic complications were included. Outcomes included number of cases and incidence of neuro-ophthalmic IRAEs. RESULTS: Neuro-ophthalmic complications of ICIs occurred in 0.46% of patients undergoing ICI and may affect the afferent and efferent visual systems. Afferent complications include optic neuritis (12.8%), neuroretinitis (0.9%), and giant cell arteritis (3.7%). Efferent complications include myasthenia gravis (MG) (45.0%), thyroid-like eye disease (11.9%), orbital myositis (13.8%), general myositis with ptosis (7.3%), internuclear ophthalmoplegia (0.9%), opsoclonus-myoclonus-ataxia syndrome (0.9%), and oculomotor nerve palsy (0.9%). Pembrolizumab was the most common causative agent for neuro-ophthalmic complications (32.1%). Mortality was highest for MG (19.8%). Most patients (79.8%) experienced improvement or complete resolution of neuro-ophthalmic symptoms due to cessation of ICI and immunosuppression with systemic corticosteroids. CONCLUSION: While incidence of neuro-ophthalmic IRAEs is low, clinicians involved in the care of cancer patients must be aware of their presentation to facilitate prompt recognition and management. Collaboration between oncology and neuro-ophthalmology teams is required to effectively manage patients and reduce morbidity and mortality.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".