Association between immune checkpoint inhibitors and uveitis in patients with lung cancer, renal cell carcinoma, or malignant melanoma
Bibliographic record
Abstract
OBJECTIVE: Immune checkpoint inhibitors (ICIs) reportedly have a potential risk of general ocular complications; however, whether ICIs have a risk of uveitis remains unclear. Therefore, we assessed whether ICI use has a higher risk of uveitis than chemotherapy alone. METHODS: Using a large administrative claims database in Japan, we identified 26 474 patients with lung cancer, renal cell carcinoma, or malignant melanoma, who initiated ICI or chemotherapy between April 2014 and November 2022. The patients were divided into 2 groups: those receiving ICI with and without chemotherapy (ICI group: n = 8103) and those receiving chemotherapy alone (non-ICI group: n = 18 371). After propensity score-overlap weighting to adjust for background factors, we estimated the incidence of uveitis and performed Cox regression analyses. We also conducted subgroup analyses stratified by age (<75 and ≥75 years). RESULTS: The overlap-weighted incidence of uveitis in the ICI group was higher than that in the non-ICI group (85.1 vs 55.9/10,000 person-years; number needed to harm: 343). The hazard ratio (HR) for uveitis in the ICI group was 1.49 (95% confidence interval, 1.11 to 2.01) in comparison with the non-ICI group. The age-stratified analysis showed that the ICI group had an increased risk among individuals aged <75 years (HR 1.65 [1.15 to 2.41]), while the risk did not differ among individuals aged ≥75 years (HR 1.35 [0.84 to 2.18]). CONCLUSIONS: ICI use was associated with a higher risk of uveitis compared to non-ICI use, particularly among patients aged <75 years.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".