The Effect of Statins on Intraocular Pressure and on the Incidence and Progression of Glaucoma: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
PURPOSE: We conducted a systematic review and meta-analysis of observational studies to evaluate the effect of oral statins on intraocular pressure (IOP) and the incidence and progression of glaucoma. METHODS: This was a systematic review of the literature and meta-analysis. Searches of PubMed/Medline and Embase were conducted to include all types of studies. Gray literature abstracts were also considered for inclusion. Last search date was February 2016. Risk of bias was assessed using the Newcastle-Ottawa scale independently by two reviewers. Odds ratios (OR) or hazard ratios (HR) and 95% confidence intervals (CI) were extracted from each study. Pooled ORs for incidence of glaucoma were calculated using a random-effects model. RESULTS: We identified seven cohort studies, three case-control studies, and one cross-sectional study with a total number of 583,615 participants. No randomized controlled trials were retrieved. Pooled ORs demonstrated a statistically significant association between short-term statin use (≤2 years) and reduced incidence of glaucoma (OR 0.96, 95%CI 0.94, 0.99). Pooled ORs of long-term statin use (>2 years) did not demonstrate statistically significant reduction in incidence of glaucoma (OR 0.70, 95%CI 0.46, 1.06). There was inconsistent evidence for the protective effect of statins against the progression of glaucoma, although there was no standard definition for progression across studies. There was no significant difference in IOP associated with statin use. CONCLUSIONS: Short-term statin use is associated with a reduced incidence of glaucoma. The effect of statins on glaucoma progression and IOP is uncertain.
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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 | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.045 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".