Risk factors for age-related macular degeneration: Updated systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Age-related macular degeneration (AMD) is a leading cause of irreversible visual loss in the elderly population, affecting millions of the people worldwide. AMD has a substantial effect on quality of life in older individuals. Understanding and identifying risk factors are crucial for developing preventive strategies. METHODS: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a comprehensive literature search across databases including PubMed, Scopus, and Web of Science up to January 28, 2024. Studies were selected using standardized inclusion and exclusion criteria, and the quality of the studies was assessed via the Newcastle Ottawa Scale. Meta-analysis was conducted using Review Manager software to pool the odds ratio (OR) of each included risk factors at the 95% confidence interval (CI). RESULTS: Eighteen of the 2640 identified studies met the inclusion criteria for the meta-analysis. Older age compared to younger age, male gender compared to female gender, smoking, hypertension, cardiovascular diseases, and diabetes were statistically significant predictors for AMD occurrence, with ORs of 1.11 (95% CI = 1.06-1.15, P < .00001), 1.63 (95% CI = 1.13-2.35, P = .009), 1.86 (95% CI = 1.33-2.6, P = .0003), 1.24 (95% CI = 1.09-1.4, P = .0007), 1.44 (95% CI = 1.11-1.87, P = .006), and 1.44 (95% CI = 1.3-1.6, P < .00001), respectively. Other factors, such as body mass index, cerebrovascular diseases, cholesterol, and triglycerides, were not significantly associated with AMD. CONCLUSION: This updated meta-analysis highlights the significance of modifiable risk factors for AMD, including smoking, hypertension, cardiovascular diseases, and diabetes. Early identification of AMD accompanied by strategic management of these modifiable risk factors may preserve patients' visual acuity without advancing to advanced stages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.047 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".