Ocular adverse events with immune checkpoint inhibitors
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Bibliographic record
Abstract
PURPOSE: To quantify the risk of ocular adverse events with immune checkpoint inhibitors (ICIs) as reported to the Food and Drug Administration (FDA). METHODS: Disproportionality analysis using data from U.S. FDA's Adverse Events Reporting System (FAERS) database 2003 to 2018. Data from pharmaceutical manufacturers, healthcare providers, consumers in the U.S., and post-marketing clinical trial reports from U.S. and non-U.S. studies. All cases of uveitis, dry eye syndrome, ocular myasthenia and eye inflammation with use of the following ICIs: atezolizumab, avelumab, cemiplimab, durvalumab, ipilimumab, nivolumab and pembrolizumab. Reported odds ratios (RORs) and corresponding 95% confidence intervals (CIs) were computed for all drugs as a group or as individual agents. RESULTS: We identified 113 ocular adverse events for all ICIs of interest including uveitis, dry eye, ocular myasthenia and eye inflammation. Nivolumab had the highest number of adverse events (N = 68) associated with use of the ICI. Nivolumab had the highest association with ocular myasthenia [ROR = 22.82, 95% CI (7.18-72.50)] followed by pembrolizumab [ROR = 20.17, 95% CI (2.80-145.20)]. Among all ICIs approved in North America, atezolizumab had the highest association with eye inflammation [ROR = 18.89, 95% CI (6.07-58.81)] and ipilmumab had the highest association with uveitis [ROR = 10.54, 95% CI (7.30-15.22)]. CONCLUSION: The results of this disproportionality analysis suggest use of ICIs is associated with an increase risk for ocular adverse reactions. Future epidemiologic studies are needed to better quantify these adverse events.
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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) | 0.000 | 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) | 0.001 | 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 it