Dyslipidemia and Diabetic Macular Edema: A Systematic Review and Meta-Analysis.
Bibliographic record
Abstract
TOPIC: A systematic review and meta-analysis of dyslipidemia and diabetic macular edema (DME). CLINICAL RELEVANCE: Diabetic macular edema causes impairment of vision in patients with diabetes, and dyslipidemia has been reported as a risk factor for its development. A systematic review with a meta-analysis was undertaken to examine the evidence of an association between dyslipidemia and DME. METHODS: We defined eligibility criteria as randomized controlled trials (RCTs) and cohort, case-control, and cross-sectional studies reporting on the relationship between blood lipid levels and DME. We performed a literature search in MEDLINE, PubMed, and Embase from inception to September 2014. We used the Newcastle-Ottawa scale to assess the quality of case-control, cross-sectional, and cohort studies, and the Cochrane risk of bias tool for RCTs. RESULTS: The search strategy identified 4959 publications. After screening, we selected 21 articles for review (5 cross-sectional, 5 cohort, 7 case-control, and 4 RCTs). Meta-analysis of case-control studies revealed that mean levels of total serum cholesterol (TC), low-density lipoproteins (LDLs), and serum triglycerides (TGs) were significantly higher in patients with DME compared with those without DME (TC: 30.08; 95% confidence interval [CI], 21.14-39.02; P < 0.001; LDL: 18.62; 95% CI, 5.80-31.43; P < 0.05; TG: 24.82; 95% CI, 9.21-40.42; P < 0.05). Meta-analysis of RCTs did not show significant risk in worsening of hard exudates and severity of DME in the lipid-lowering group compared with placebo (hard exudates: relative risk, 1.00; 95% CI, 0.47-2.11; P = 1.00; DME: relative risk, 1.18; 95% CI, 0.75-1.86; P = 0.48). CONCLUSIONS: Despite evidence from the cohort studies and meta-analysis of the case-control studies suggesting a strong relationship between lipid levels and DME, this was not confirmed by the meta-analysis that included only prospective RCTs. Therefore, given the significant public health relevance of the topic, the relationship between lipid levels and DME deserves further investigation.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.030 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".