Use of Lipid-Lowering Agents for the Prevention of Age-Related Macular Degeneration: A Meta-Analysis of Observational Studies
Bibliographic record
Abstract
PURPOSE: To examine the effect of lipid-lowering agents in the development of age-related macular degeneration (AMD) through the techniques of meta-analysis. METHODS: Case-control and cohort studies presenting relative risks and 95% confidence intervals were identified through a literature review. Inclusion was limited to studies where both the exposure of interest (lipid-lowering agents) and outcome (AMD) were explicitly defined. Pooled estimates were computed using the random effects model. To quantify heterogeneity we calculated the proportion of total variance of between study variance using the Ri statistic. The Q statistic for heterogeneity was also calculated. RESULTS: Eight studies were identified. The pooled relative risk (RR) for all studies was 0.74 (95% CI, 0.55-1.00). When only those studies examining the use of statins were pooled (n=7), the RR was 0.70 (95% CI, 0.48-1.03). Using the Ri statistic, the heterogeneity between studies was found to be 0.85 for all studies and 0.89 for studies examining statins. CONCLUSION: Lipid-lowering agents, including statins, do not appear to lower the risk of developing AMD, although clinically significant effects cannot be excluded. The use of these agents in the prevention of AMD cannot be recommended until well designed prospective studies with long follow up have demonstrated a benefit.
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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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.025 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".