Repeat Posterior Lamellar Grafting for Recalcitrant Lower Eyelid Retraction Is Effective
Bibliographic record
Abstract
PURPOSE: To review one surgeon's (J.H.O.) experience with repeat retractor release and posterior lamellar grafting in patients with residual lower eyelid retraction. To quantify the amount of eyelid elevation expected from each procedure. METHOD: Retrospective chart review of patients with repeat posterior lamellar grafting between 1992 and 2010. Patients were grouped into thyroid associated orbitopathy (TAO) and other causes. Hard palate mucosa or free tarsoconjunctiva grafts were used. Preoperative and postoperative inferior scleral show, lagophthalmos, superficial punctate keratopathy, and patient symptoms were recorded. Outcome measures were changes in scleral show and lagophthalmos with each procedure. Combined results were examined.Results in patients with TAO were analysed separately and compared with other etiologies. RESULTS: In this series, a single procedure is expected to reduce scleral show by a mean of 1.63 mm (76%) and lagophthalmos by a mean of 0.48 mm (55%). A second procedure can further reduce residual scleral show by a mean of 0.71 mm (80%) and residual lagophthalmos by a mean of 0.43 mm (76%). Patients with TAO were more likely to have larger measurements of preoperative scleral show (1.40 mm versus 0.46 mm, p < 0.001). Patients with other etiologies were more likely to have larger measurements of preoperative lagophthalmos (1.25 mm versus 0.47 mm, p = 0.004). CONCLUSIONS: This is the first study to evaluate outcomes of recalcitrant lower lid retraction requiring repeat posterior lamellar grafting. Mean reductions in scleral show and lagophthalmos can be used as a guide in the preoperative evaluation and counseling of patients with lower lid retraction.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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, 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".