Global epidemiology of retinal vein occlusion: a systematic review and meta-analysis of prevalence, incidence, and risk factors
Bibliographic record
Abstract
BACKGROUND: Retinal vein occlusion (RVO) is the second most common retinal vascular disorder that affected 16.4 million people worldwide in 2008. The last decade has seen new epidemiological data on RVO, enabling us to provide a contemporary estimation of RVO epidemiology. METHODS: We searched PubMed, Medline, Embase, GLOBAL HEALTH, World Health Organization Global Health Library, China National Knowledge Infrastructure for studies that reported prevalence or incidence of RVO in the general population. The age- and sex-specific prevalence of RVO was estimated by a multilevel mixed-effects logistic regression, the incidence of RVO and potential risk factors for RVO were respectively pooled by a random-effects meta-analysis. RESULTS: The prevalence of any RVO, branch RVO (BRVO) and central RVO (CRVO) all increased with advanced age, but didn't differ significantly between sexes. In 2015, the global prevalence of any RVO, BRVO and CRVO in people aged 30-89 years was 0.77% (95% confidence interval CI = 0.55-1.08), 0.64% (95% CI = 0.47-0.87) and 0.13% (95% CI = 0.08-0.21), equivalent to an overall of 28.06 million, 23.38 million and 4.67 million affected people. For any RVO, the pooled five-year cumulative incidence was 0.86% (95% CI = 0.70-1.07) and the pooled ten-year cumulative incidence was 1.63% (95% CI = 1.38-1.92). Hypertension was the strongest risk factor for any RVO, with a meta- odds ratio (OR) of 2.82 (95% CI = 2.12-3.75). CONCLUSIONS: This study provides an updated summary of RVO epidemiology in the general population. More epidemiological studies worldwide are still needed to better understand the global disease burden of RVO.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".