Changes in Tear Meniscus and Corneal Epithelial Thickness Following Upper Blepharoplasty: A Systematic Review of Clinical Evidence from Anterior Segment OCT Studies
Bibliographic record
Abstract
Background: Upper eyelid blepharoplasty is a prevalent surgical intervention for dermatochalasis, but its effects on tear film stability and corneal epithelial thickness (CET) remain debated. Anterior Segment Optical Coherence Tomography (AS-OCT) provides precise, non-invasive measurements of tear meniscus parameters (TMH, TMA) and CET, offering insights into postoperative ocular surface changes. Objective: This systematic review synthesizes clinical evidence from AS-OCT studies to evaluate changes in tear meniscus and CET following upper blepharoplasty. Methods: A literature search has been performed in PubMed, Scopus, and Web of Science following PRISMA 2020 guidelines. Inclusion criteria comprised clinical researches reporting pre- and postoperative AS-OCT measurements of TMH, TMA, or CET. Five studies (prospective and observational) met eligibility criteria, with sample sizes ranging from 25 to 60 cases. Methodological quality has been evaluated utilizing the Newcastle-Ottawa Scale. Results: Postoperative increases in TMH and superior CET were statistically significant, suggesting reduced mechanical pressure from the upper eyelid. TMA and central CET showed inconsistent changes. Transient tear film alterations were noted, with normalization by three months. AS-OCT demonstrated high reproducibility in tracking these metrics. Conclusion: Upper blepharoplasty may improve tear film dynamics and superior corneal epithelial remodeling, as quantified by AS-OCT. However, variability in outcomes underscores the need for larger, standardized studies to confirm these benefits and optimize surgical approaches for ocular surface health.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".