Association between Dyslipidemia and Meibomian Gland Dysfunction: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
PURPOSE: This study aimed to use meta-analysis to estimate the association between dyslipidemia and meibomian gland dysfunction. METHODS: The following databases were searched: PubMed, Google Scholar, and Scopus. Case-control and cohort studies assessing the association between dyslipidemia and meibomian gland dysfunction were included. The association was assessed using odds ratios. Heterogeneity between studies was assessed with the χ2 statistic and degree of inconsistency. The quality of studies was assessed using the Newcastle-Ottawa Scale. The systematic review was registered on PROSPERO (ID: CRD42022347982). RESULTS: The systematic review included three case-control and two cohort studies. The odds of hypercholesterolemia and hypertriglyceridemia in meibomian gland dysfunction were 5.45 (95% confidence interval [CI], 1.65 to 17.95) and 3.28 (95% CI, 1.25 to 8.62), respectively. The odds of elevated serum low-density lipoprotein and reduced high-density lipoprotein in meibomian gland dysfunction were 2.72 (95% CI, 1.24 to 5.98) and 1.15 (95% CI, 0.74 to 1.79), respectively. The current study's limitation is that the effects of sex, age, and meibomian gland dysfunction severity on the association between dyslipidemia and meibomian gland dysfunction were not assessed. CONCLUSIONS: The current study suggests a significant association between dyslipidemia and meibomian gland dysfunction. This finding suggests that meibomian gland dysfunction diagnosis may call for dyslipidemia screening.
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.041 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".