Association between Diabetes and Exfoliation Syndrome
Bibliographic record
Abstract
Topic This systematic review and meta-analysis summarizes the existing evidence for the association of diabetes mellitus (DM) and exfoliation syndrome (XFS). Clinical Relevance Understanding and quantifying these associations may aid clinical guidelines or treatment strategies and shed light on disease pathogenesis. The role of DM in determining XFS risk may also be of interest from an individual or public health perspective. Methods The study protocol was pre-registered on the International Prospective Register of Systematic Reviews (PROSPERO) with registration number CRD42023429771. We systematically searched PubMed and Embase from inception to June 15, 2023. Screening and full-text review were conducted by two independent reviewers. All observational studies reporting an age-adjusted odds ratio (OR) and 95% confidence interval (CI) for the association between DM and XFS among adults were included. Quantitative synthesis involved a random-effects meta-analysis using the DerSimonian-Laird method to generate a pooled OR. Risk of bias was evaluated using the Newcastle-Ottawa Scale (NOS). Results Fourteen studies (9 cross-sectional and 5 case-control) comprising 47,853 participants were included in the systematic review and meta-analysis. Random-effects meta-analysis indicated no overall association between DM and XFS (OR 0.94; 95% CI, 0.73-1.21; I 2 = 68.5%). However, subgroup analysis revealed a significant inverse association among individuals ≥65 years (OR 0.71; 95% CI, 0.54-0.93) versus individuals <65 years (OR 1.22; 95% CI, 0.80-1.87; P effect modification = 0.04). The relation between DM and XFS was also inverse in case-control studies (OR 0.75; 95% CI, 0.58-0.97) but was non-significant in cross-sectional studies (OR 1.17; 95% CI, 0.83-1.66; P effect modification = 0.04). Overall risk of bias was low, with tests for publication bias showing P ≥ 0.06. Conclusion This meta-analysis suggests no association between DM and XFS overall, with possible inverse associations of DM with XFS in older populations. However, given the substantial heterogeneity and borderline significance for publication bias, these findings should be interpreted with caution. Our results give insight into the unique etiology and clinical relevance of XFS while proposing the need for larger longitudinal and genetic biomarker studies.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.015 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".