A Modified Posterior Approach for Upper Eyelid Retraction
Bibliographic record
Abstract
PURPOSE: To describe and review a graded technique for lowering the upper eyelid from a posterior approach, recessing or resecting Müller muscle and levator aponeurosis but leaving a central pillar of Müller muscle intact, occasionally including a temporal tarsorrhaphy or superior tarsal strip where needed, and performing tissue dissection with a hot-wire cautery instrument. METHODS: A 10-year retrospective chart review was performed. Where follow-up was less than 6 months, telephone interviews were conducted to assess patient satisfaction with the procedure. Statistical analysis was performed using an unpaired t test. RESULTS: Ninety-nine patients (161 eyelids, 62 bilateral and 37 unilateral) with a mean age of 47 years (range, 21-82 years) were studied. The mean follow-up period was 61 months. The mean preoperative and postoperative margin reflex distances (MRD1) were 7.3 mm (range, 4.5-10 mm) and 4.3 mm (range, 2-7 mm), respectively. Eighty-nine percent (144/161 eyelids) achieved the target result of an MRD1 of 4 +/- 1 mm after one procedure. Fifteen eyelids (9% of operated eyelids) required a second procedure, and in this group, 2 (13% of the reoperated eyelids) underwent a third procedure. Although bilateral cases were more likely to achieve symmetry (p = 0.0071), 90% of either unilateral or bilateral cases achieved a postoperative MRD1 of 4 +/- 1 mm. Both mild (MRD1 of 5-7 mm) and severe (MRD1 > 7 mm) cases of eyelid retraction achieved similar operative outcomes. In the first 6 months after surgery, complications included undercorrection (8 eyelids), overcorrection (2 eyelids), and pyogenic granuloma (2 eyelids). None had a flattened upper eyelid contour. Late recurrence of retraction was seen in 9 eyelids. Mean operative time was 16 minutes per eyelid. CONCLUSIONS: This technique of lowering the retracted upper eyelid is effective even in severe cases of eyelid retraction. Minimal complications were encountered, and upper eyelid contours were well preserved. The use of hot-wire cautery dissection proved useful in shortening operative time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".