A real-world population-based study on the association between cataracts and antipsychotics
Bibliographic record
Abstract
Objective It is uncertain whether first- (FGAs) and second-generation antipsychotic (SGAs) drugs are associated with development of cataract. We conducted a real-world adverse event (AE) disproportionality analysis of cataracts in individuals exposed to antipsychotics. Design An observational, population-based pharmacovigilance study. Participants All reports of cataracts submitted to the U.S. Food and Drug Administration involving FGAs and SGAs. Methods We searched the United States Food and Drug Administration Adverse Event Reporting System (FAERS) (2003 Q4–2024 Q1) for cataract reports associated with antipsychotics, analyzing data with OpenVigil 2.1. Reporting odds ratios (RORs), p values (with a Bonferroni-adjusted significance threshold of 0.0019), and Bayesian Confidence Propagation Neural Network information components (IC) were reported. Results We retrieved 12,345,128 unique AE reports (5.4% females, 34.5% males), including 34,879 cataract reports and 372,107 antipsychotic reports (10,274 FGAs and 361,833 SGAs). Chlorpromazine (ROR: 7.60; 95%; confidence interval [CI] = 3.78%–15.29%; p < 0.0001; [lower limit of the 95% credibility interval for the information component] IC 025 = 1.23) and quetiapine (ROR1: 64; 95% CI = 1.44%–1.88%; p < 0.0001; IC 025 = 0.48) showed significantly higher odds of cataract reporting compared to all other drugs. No significantly higher reporting odds of cataracts were found for the 10 FGAs and 14 SGAs studied. However, increased cataract reporting was observed for chlorpromazine in females and for quetiapine in both sexes. This pattern also occurred in both drugs in the 18–44 and 45–64 age groups, with greater magnitude in the younger group Conclusions Cataract development was disproportionately reported after the use of certain FGAs and SGAs. Although a cause-and-effect relationship cannot be established, these findings underscore the importance of clinical vigilance and regular ocular monitoring in individuals prescribed antipsychotics.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".