Surgical Management of Large Periorbital Cutaneous Defects
Bibliographic record
Abstract
PURPOSE: The aim of this study was to repair large periorbital cutaneous defects by an innovative technique called PEPSI (periorbital elevation and positioning with secret incisions) technique with functional and aesthetic outcomes. METHODS: In this retrospective study, unilateral periorbital cutaneous defects in 15 patients were repaired by the PEPSI technique. The ages of patients ranged from 3 to 46 years (average, 19 years). The outcome evaluations included scars (Vancouver Scar Scale and visual analog scale score), function and aesthetic appearance of eyelids, and patient satisfaction. The repair size was measured by the maximum advancement distance of skin flap during operation. RESULTS: All patients achieved an effective repair with a mean follow-up of 18.3 months. Except one with a small (approximately 0.3 cm) necrosis, all patients healed with no complication. The mean Vancouver Scar Scale and visual analog scale scores were 2.1 ± 1.7 and 8.5 ± 1.2, respectively. Ideal cosmetic and functional outcomes were achieved in 14 patients (93.3%). All patients achieved complete satisfaction except 1 patient with partial satisfaction. The mean maximum advancement distance of skin flap was 20.2 mm (range, 8-50 mm). CONCLUSIONS: This study demonstrated that the PEPSI technique is an effective method to repair large periorbital cutaneous defects with acceptable functional and aesthetic outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.002 | 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".