Risk factors of retinal vein occlusion in East Asia: a meta-analysis
Bibliographic record
Abstract
Objective With the intention of developing a more targeted control strategy for retinal vein occlusion (RVO) in East Asian populations, a meta-analysis was conducted to evaluate the risk factors associated with RVO in this region. Methods PubMed, Web of Science, Cochrane Library, CNKI, Wanfang and VIP databases were searched for studies that reported risk factors of RVO in East Asia, published from the establishment of the database to May 2024. To further filter the articles, Newcastle-Ottawa Scale (NOS) evaluation method was utilized to assess the quality of selected articles. After valid data were extracted, Meta-analysis was performed by Review Manager software. Results A total of 21 literatures were included, including 27561 cases in the RVO group (Case group) and 514578 cases in the non-retinal vein occlusion (NRVO) group (Control group). Results of meta-analysis showed that chronic kidney disease [odds ratio (OR)=4.14, 95% confidence interval (CI): (1.86%, 9.24%)], hypertension [OR=4.11, 95% CI: (3.09%, 5.48%)], hyperlipidemia [OR=3.45, 95%CI: (2.32%, 5.12%)], diabetes mellitus [OR=3.00, 95%CI: (1.88%, 4.80%)], homocysteine [OR=0.87, 95%CI: (0.59%, 1.15%)], have statistically significant differences between the RVO group and the NRVO group(P<0.05). Conclusion The occurrence of RVO is closely related to its risk factors, such as chronic kidney disease, hypertension, hyperlipidemia, diabetes mellitus and high homocysteine. In the process of diagnosis and treatment of RVO, doctors should focus on the above risk factors to prevent the occurrence of the disease.
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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.046 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".