Variations of dry eye disease prevalence by age, sex and geographic characteristics in China: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Dry eye disease (DED) is one of the most prevalent ocular diseases in the world. In China, new lifestyles driven by information technology and the rapid ageing process have brought DED a severe public health concern. The aim of our study was to obtain the pooled prevalence of DED in China and explore its potential correlates. METHODS: A comprehensive systematic review was conducted to identify all relevant literature published since 1990. Meta-analysis and meta-regression approaches were adopted to estimate the prevalence of DED. The number of people with DED was obtained by multiplying the corresponding demographic data in 2010. RESULTS: Advanced age, female sex and larger latitude were significant risk factors for DED by symptoms and signs, whereas only advanced age was positively associated with an increased prevalence of DED by symptoms. In 2010, the prevalence of DED by symptoms and signs were 13.55% (95% CI = 10.00-18.05) and that of DED by symptoms was 31.40% (95% CI = 23.02-41.13) in Chinese people aged 5-89 years, corresponding to a total of 170.09 million (95% CI = 125.52-226.63) and 394.13 million (95% CI = 288.99-516.30) affected individuals respectively. CONCLUSIONS: The huge burden of DED in China calls for more public health attention and actions. Improved epidemiological studies on DED prevalence are still urgently needed.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.027 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".