Global prevalence and risk factors of primary open-angle glaucoma: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: POAG is a major cause of irreversible blindness globally, characterized by progressive optic nerve damage and visual field loss.Despite its association with intraocular pressure (IOP), many patients develop the disease within the normal IOP range, highlighting its multifactorial nature.Aim and Objective: To determine the global prevalence and risk factors of primary open-angle glaucoma (POAG) and answer the research question: "What demographic, clinical, and methodological factors most significantly influence the prevalence of POAG across populations worldwide?"Materials and Methods: A systematic review and meta-analysis were conducted on studies published from January 2014 to March 2025 across PubMed, Embase, and Scopus.Ten studies with a combined sample of 839,940 participants were included.Data extraction focused on prevalence, demographics, and risk factors, and analysis was performed using SPSS and R Studio.Study quality was assessed via the Newcastle-Ottawa Scale.Results: The mean pooled prevalence of POAG was 3% (range 1.9-31.7%).Major risk factors identified included advancing age, elevated IOP, hypertension, diabetes, family history, myopia, and polygenic susceptibility.Considerable heterogeneity was noted (p < 0.001).Conclusion: POAG is a heterogeneous, multifactorial disease.Integration of genetic risk profiling, AI-based screening, and early detection strategies can enhance prevention and reduce the global burden of blindness.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.031 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".