Evaluation of Esthetic Results after Mass Removal with Elliptical Skin Excision Using Ultrasonography to Measure Skin Thickness
Bibliographic record
Abstract
Background: The growth of benign cutaneous masses causes the overlaying skin to expand and become thinner, especially at the central, most projected point. In this retrospective study, a surgical technique comprising an elliptical skin excision was employed to account for these skin changes. Methods: This retrospective study enrolled 980 patients with benign masses. Preoperatively, all patients underwent ultrasonography to evaluate the mass depth and thickness of the attached skin, and mass excision was performed using the elliptical skin-excision method. The operative time was recorded, and complications and esthetic outcomes were assessed using the Cutometer® and the modified Vancouver Scar Scale (mVSS) during 1- and 3-month follow-up visits. Results: The mean operative time (17.48 ± 3.46 min) was significantly shorter than that of conventional methods (p < 0.05). Cutometer parameters showed no significant differences from those of intact skin. The average mVSS scores were 5.21 ± 1.42 and 3.50 ± 1.79 at 1- and 3-month follow-ups, respectively. Conclusions: Mass excision with an elliptical skin attachment resulted in improved esthetic results and easy removal. The attached skin enabled convenient handling without damaging the capsule or other adjacent structures, leaving a thick dermis on both wound edges. Thus, this technique resulted in minimal scarring.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".