Use of Diuretics and Risk of Acute Angle Closure: A Case-Control Study
Bibliographic record
Abstract
Purpose To examine the possible link between acute angle closure (AAC) with use of diuretics.Methods A nested case-control study (NCC) was conducted among a cohort of diuretic users using the PharMetrics Plus database from 2006 to 2020. Cases were identified as the first international classification of diseases 9th and 10th editions (ICD-9/10) code for ACC. For each case, 4 controls were selected and matched to the cases by age and sex using density-based sampling. A conditional logistic regression model was used to compute rate ratios (RRs) adjusted for the drugs topiramate, bupropion, sulphonamide antibiotics, acetazolamide, and sulfasalazine. The RRs for a negative control drug, amlodipine, was also assessed.Results From the initial cohort of 713 574 diuretics users, 1 553 cases and 6 212 controls were identified. No increase in the risk of AAC with current users of diuretics was found (RR = 1.06, (95% CI: 0.81–1.37) for all diuretics; RR = 0.97, (95% CI: 0.71–1.32) for thiazides; RR = 1.24, (95% CI: 0.90–1.73) for loop diuretics; RR = 0.99, (95% CI: 0.73–1.36) for potassium sparing).Conclusion We found no increase in the risk of acute angle closure with use of diuretics. Future studies are needed to confirm these findings.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".