EVALUATING THE LONG-TERM EFFECTS OF GAS PERMEABLE LENSES ON CORNEAL ENDOTHELIAL: A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
Abstract Introduction: Rigid Gas permeable (GP) lenses are widely used for vision correction, but their long-term effects on the corneal endothelium remain uncertain. This systematic review and meta-analysis aimed to evaluate the impact of GP lens wear on corneal endothelial cell density, morphology, and function. Methods: A systematic search of PubMed, Scopus, Web of Science, and Cochrane Library identified cohort and case-control studies reporting endothelial cell density, morphology, and pleomorphism in individuals wearing GP lenses for over six months. Two reviewers independently extracted data, and the Newcastle-Ottawa Scale was used to assess bias risk. A random-effects model meta-analysis calculated pooled estimates of GP lenses' effects on endothelial parameters. Results: Meta-analysis comparing GP lenses to controls revealed no significant differences in endothelial parameters. For endothelial cell density, the mean difference was 4.40 (p = 0.207, Confidence Interval (CI): -2.43 to 11.24, I² = 99.62%). Endothelial coefficient of variation (ECV%) showed a mean difference of -5.60 (p = 0.245, CI: -15.03 to 3.84, I² = 99.77%), while endothelial hexagonality (HXG%) had a mean difference of -3.15 (p = 0.190, CI: -11.26 to 4.96, I² = 97.45%). Substantial heterogeneity across studies limits definitive conclusions. Conclusion: GP lenses do not significantly impact corneal endothelial parameters, including ECD, ECV%, and HXG%, compared to controls. Further research is required to validate these findings and assess long-term effects on endothelial integrity.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.031 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".