Short-Term Exposure to Antidepressant Drugs and Risk of Acute Angle-Closure Glaucoma Among Older Adults
Bibliographic record
Abstract
Acute angle-closure glaucoma (AACG) is an ocular emergency that may be precipitated by certain types of medications. Antidepressant drugs can affect a number of neurotransmitters, which are involved in the regulation of the iris, which may precipitate AACG. We used a case-crossover study design to investigate the association between recent exposure to antidepressant drugs and AACG. We identified patients with AACG among adults aged 66 years or older between 1998 and 2010 in Ontario using linked population-based administrative databases. We identified intermittent users of antidepressant medications through prescription drug claims in the year preceding AACG. We determined antidepressant exposure in the period immediately before AACG and compared it with antidepressant exposure in 2 earlier control periods. We used conditional logistic regression to determine the odds ratio for antidepressant exposure in the hazard period compared with the control periods. A total of 6470 patients with AACG occurred during the study period. The mean age of the patients was 74.3 years, and 66% were female. Overall, 5.6% of individuals were intermittent users of antidepressant drugs in the year preceding AACG. The odds ratio for any antidepressant exposure in the period immediately preceding AACG was 1.62 (95% confidence interval, 1.16-2.26). An increased risk of AACG was also observed in several subgroups. We conclude that recent exposure to antidepressant drugs is associated with an increased risk of AACG. Clinicians should remain vigilant for the development of this uncommon but potentially serious adverse event after initiating antidepressant therapy.
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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 | 0.004 |
| 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".