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Record W4411299197 · doi:10.1016/j.jcjo.2025.05.021

A real-world population-based study on the association between cataracts and antipsychotics

2025· article· en· W4411299197 on OpenAlexafffundvenue
Moiz Lakhani, Angela T.H. Kwan, Emaan Chaudry, Marko M. Popovic, Jim Shenchu Xie, Amrit Rai, Amandeep Rai, Edward Margolin, Roger S. McIntyre

Bibliographic record

VenueCanadian Journal of Ophthalmology · 2025
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacovigilance and Adverse Drug Reactions
Canadian institutionsUniversity of TorontoUniversity of OttawaBrain and Cognition Discovery Foundation
FundersCanadian Institutes of Health ResearchAlconNational Natural Science Foundation of ChinaLife Sciences, University of California, Los AngelesFoundation Fighting BlindnessGlobal Alliance for Chronic DiseasesAbbViePhysicians' Services Incorporated Foundation
KeywordsQuetiapineMedicineOdds ratioCataractsConfidence intervalAntipsychoticAdverse Event Reporting SystemInternal medicinePopulationPharmacovigilanceAdverse effectPediatricsPsychiatrySchizophrenia (object-oriented programming)OphthalmologyEnvironmental health

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.993
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.122
GPT teacher head0.451
Teacher spread0.328 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2025
Admission routes3
Has abstractyes

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