Bibliographic record
Abstract
Objective To evaluate the risk factors for retinal vein occlusion(RVO) through a meta-analysis. Methods The literatures on risk factors of RVO were searched in the Cochrane library, PubMed, and Embase databases.The literature search time ranged from the establishment of database to December 2018.The literatures were evaluated and filtrated by using Newcastle-Ottawa Scale, Stata software (version 12.0) was used for data processing. Results A total of 31 case-control studies with 4 370 cases and 6 534 controls were included.The meta-analysis showed that hypertension (odds ratio[OR]=3.08, 95% confidence interval [CI]: 2.22-4.28), diabetes (OR=1.61, 95% CI: 1.11-2.32), hyperlipidemia (OR=1.73, 95% CI: 1.27-2.36), hyperlipoprotein (a)-emia (OR=2.72, 95% CI: 1.06-6.97), hyperhomocysteinemia (OR=1.86, 95% CI: 1.47-2.35), mutation of coagulation factor V Leiden gene (OR=1.89, 95% CI: 1.17-3.06) were risk factors for RVO.However, mutation of gene MTHFR C677T (OR=1.41, 95% CI: 0.93-2.14)、mutation of prothrombin gene G20210A (OR=1.20, 95% CI: 0.81-1.79) were not found to be risk factors for RVO.Subgroup analysis showed that the heterogeneity of hypertension and diabetes among people aged over 60 decreased from 88.9% and 75.7% to 59.8% and 63.2%, respectively.The heterogeneity of hyperhomocysteinemia in people aged below 60 decreased from 85.6% to 64.3%.The sensitivity analysis results showed that there were no significant differences after changing the analysis model.There was no publication bias among the literatures. Conclusions Hypertension, diabetes, hyperlipidemia, hyperhomocysteinemia, hyperlipoprotein (a)-emia, mutation of coagulation factor V Leiden gene are risk factors for retinal vein occlusion. Key words: Retinal vein occlusion; Risk factors; Meta-analysis
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".