Rethinking Upper Blepharoplasty: The Impact of Pretarsal Show
Bibliographic record
Abstract
BACKGROUND: Conventional upper blepharoplasty relies on skin, muscle, and fat excision to restore ideal pretarsal space-to-upper lid fold ratios. The purpose of this study was to identify presenting topographic features of upper blepharoplasty patients and their effect on cosmetic outcomes. METHODS: This is a retrospective review of patients who underwent upper blepharoplasty at the authors' institution from 1997 to 2017. Preoperative and postoperative photographs were standardized using Adobe Illustrator to an iris diameter of 11.5 mm. Pretarsal and upper lid fold heights were measured at five locations. Patients were classified into three groups based on preoperative pretarsal show: none, partial, or complete. Photographs were randomized in PowerPoint and given a cosmetic score of 0 to 5 by four independent reviewers. RESULTS: Three hundred sixteen patients were included, 42 men (13 percent) and 274 women (87 percent). Group 1 included 101 eyes (16 percent), group 2 had 159 eyes (25 percent), and group 3 had 372 eyes (59 percent). Mean cosmetic score increased from 1.75 to 2.38 postoperatively (p < 0.001), with a significantly lower improvement in scores in group 3 compared to groups 2 and 1 for both sexes (p < 0.01). For group 3, those with midpupil pretarsal heights greater than 4 mm had a significantly lower postoperative aesthetic score (1.95) compared with those less than or equal to 4 mm (2.50) (p < 0.001). CONCLUSIONS: Many patients presenting for upper blepharoplasty have complete pretarsal show and are at risk for worse cosmetic outcomes using conventional skin excision techniques. Adjunctive procedures such as fat grafting and ptosis repair should be considered in this group. CLINICAL QUESTION/LEVEL OF EVIDENCE: Risk, II.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".