The association between immune checkpoint or BRAF/MEK inhibitor therapy and uveitis in patients with advanced cutaneous melanoma
Bibliographic record
Abstract
BACKGROUND: Treatment with immune checkpoint and BRAF/MEK inhibitors has significantly improved the survival of patients with advanced cutaneous melanoma and other metastatic malignancies. Therapy-related uveitis is a rare ocular adverse event, which may potentially lead to legal blindness. The epidemiology of treatment-related uveitis is currently insufficiently known. PATIENTS AND METHODS: In this cohort study, we asked whether exposure to either immune checkpoint or BRAF/MEK inhibitors was associated with a higher risk of developing uveitis compared with the general population. Based on a Bayesian framework, we estimated the probability of developing uveitis with a right-censored, exponential survival model using data from the Zurich Melanoma Registry. The registry included all adult patients treated for advanced cutaneous melanoma between January 2008 and December 2018 at the University Hospital of Zurich, Switzerland. RESULTS: In total, 304 patients (64%) were treated with immune checkpoint and 186 patients (38%) with BRAF/MEK inhibitors. Median follow-up time was 74 days (interquartile range: 57-233 days). Eleven patients developed uveitis and 30 patients died. We estimated the probability of developing uveitis per year in the general population as 0.05% (95% credibility interval [CrI]: 0.02%-0.1%). Corresponding posterior probabilities of treatment-related uveitis were 3.48% (95% CrI: 0.93%-7.49%) and 5.04% (95% CrI: 2.07%-9.19%) for immune checkpoint or BRAF/MEK inhibitors (posterior probability for difference: 76%). CONCLUSIONS: Immune checkpoint and particularly BRAF/MEK inhibitor therapies are associated with an increase in the risk of developing uveitis. Treatment-related uveitis is not associated with systemic adverse events of immune checkpoint or BRAF/MEK inhibitors.
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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) | 0.000 | 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) | 0.000 | 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".