Association of Gabapentin or Pregabalin Use and Incidence of Acute Angle-closure Glaucoma
Bibliographic record
Abstract
PRECIS: Gabapentin and its derivatives have numerous indications and are commonly prescribed medications. In this article, we provide evidence of a link between gabapentinoid use and incidence of acute angle-closure glaucoma. PURPOSE: Gabapentinoids, such as gabapentin and pregabalin, are commonly prescribed classes of drugs in North America. We sought to determine the association of gabapentin or pregabalin use and the incidence of acute angle-closure glaucoma. MATERIALS AND METHODS: This was a nested case-control study. All adult patients who developed acute angle-closure glaucoma between January 1, 2006 and December 31, 2016, and enrolled in the PharMetrics Plus database were eligible for inclusion. A conditional logistic regression model was constructed to assess the association between gabapentin or pregabalin use and the incidence of acute angle-closure glaucoma. RESULTS: Incidence of acute angle-closure glaucoma was found to be statistically significantly associated with the use of gabapentin in the year before diagnosis [relative risk (RR), 1.42; 95% confidence interval (CI), 1.00-2.00]. This association was not observed to be statistically significant with the current use of gabapentin (RR, 1.28; 95% CI, 0.77-2.12). Incidence of acute angle-closure glaucoma (AAG) was not found to be statistically significantly associated with either use of pregabalin in the year before diagnosis or current use (RR, 1.00; 95% CI, 0.51-1.93 and RR, 1.50; 95% CI, 0.66-3.38, respectively). CONCLUSIONS: To the best of our knowledge this is the first study to investigate the association between gabapentin or pregabalin use and the incidence of AAG. Gabapentin use in the year before diagnosis was found to be associated with the incidence of AAG.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".