Treatment of Lower Eyelid Retraction by Retractor Release and Posterior Lamellar Grafting: An Analysis of 659 Eyelids in 400 Patients
Bibliographic record
Abstract
PURPOSE: To review one surgeon's experience with posterior lamellar grafting for lower eyelid elevation over a 15-year period, comparing the success of different posterior lamellar grafts used in 4 etiology groups. METHODS: A retrospective chart review of 400 patients (659 eyelids) was conducted. Patients were grouped into thyroid ophthalmopathy, previous surgery, trauma, and idiopathic causes. Three graft types were used: hard palate mucosa, free tarsoconjunctival, and free scleral. Objective measurements of lagophthalmos, scleral show, and superficial punctate keratopathy, and subjective patient symptoms, preoperatively and postoperatively were compared between graft types and etiologic groups. Complications were tabulated and compared between groups, as was any need for further surgery. The mean follow-up interval was 16.5 months. The main outcome measures were objective measurements of lagophthalmos, scleral show, and superficial punctate keratopathy. RESULTS: A mean reduction in lagophthalmos ( approximately 0.5 mm), superficial punctate keratopathy (mean score reduction = 0.2, on a scale of 1-3), and scleral show ( approximately 1.3 mm) was demonstrated for all etiology groups and graft types. Furthermore, 90% of patients subjectively reported a reduction of 1 to 3 symptoms. Hard palate mucosa grafts were more likely to be used than tarsoconjunctival grafts in cases with one or more previous surgeries (p < 0.001). Complications were more common with tarsoconjunctival grafts (except for bleeding), but the difference was statistically significant only for wound dehiscence (p = 0.004). CONCLUSIONS: Lower eyelid retraction repair with posterior lamellar grafting and lateral eyelid tightening can be recommended with confidence for eyelid retraction patients because most improved subjectively and by objective examination.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".