Using super tension-relieving suture technique combined with W-plasty for facial scar repair: a retrospective comparative study
Bibliographic record
Abstract
PURPOSE: This study aimed to evaluate the efficacy of combining the super tension-relieving suture technique with W-plasty in the repair of facial scars, comparing its outcomes to conventional suture methods. METHOD: A retrospective analysis was conducted on 81 patients with facial scars treated at the Department of Plastic and Aesthetic Surgery, Second Affiliated Hospital of Soochow University, between January 2022 and September 2024. Patients were divided into two groups: one received the combined super tension-relieving suture and W-plasty technique, while the other underwent conventional scar excision and linear closure. Scar quality was assessed preoperatively and six months postoperatively using the Patient and Observer Scar Assessment Scale (POSAS) and the Vancouver Scar Scale (VSS). RESULT: Postoperative evaluations revealed improvements in scar quality for both groups. However, the combination method group showed a trend toward improved outcomes compared with the control group, including better scores on the VSS scale (pigmentation, P< 0.05 after FDR correction) and POSAS scale (color and pigmentation, P < 0.05 after FDR correction). The total scores in VSS, PSAS and OSAS scale were significantly lower in the combination group (P < 0.05 after FDR correction), indicating enhanced scar appearance and patient satisfaction. CONCLUSION: The combination of the super tension-relieving suture technique and W-plasty is an effective approach for facial scar repair, yielding superior aesthetic and functional results compared to conventional methods. This technique addresses mechanical tension and aligns with natural skin lines, making it a promising option for scar revision in plastic and reconstructive surgery.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".