Continuous tension reduction technique in facial scar management: A comparison of W‐plasty and straight‐line closure on aesthetic effects in Asian patients
Bibliographic record
Abstract
W-plasty is a very popular scar excisional revision technique. The core of the technique is to break up the scar margins into small triangular components, so as to cause light scattering and make the scar less noticeable. However, due to skin tension, facial incision scars tend to spread. Applying W-plasty alone cannot achieve the ideal repair effect of facial scars. In this study, we proposed a scar revision technique combined W-plasty with continuous tension-reduction (CTR) technique to improve the appearance of facial scars. Sixty patients with facial scar were comprised in this retrospective study. Scars were assessed independently using the scar scale before and at 12-month follow-up. Clinical results showed a significant difference in scar appearance between different groups at 12-month follow-up. Vancouver scar scale (VSS), visual analogue scale (VAS) scores, and patient satisfaction were significant better in W-plasty and CTR than other groups at 12-month follow-up. No severe complications were reported. The application of the tension offloading device provides an environment where the tension is continuously reduced, which could greatly decrease tension on the surgical incision. Combined with W-plasty, this technique could significantly improve the scar's aesthetic appearance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".