Various Applications of Purse-String Suture and Its Cosmetic Outcome in Cutaneous Surgical Defects
Bibliographic record
Abstract
Background: Purse-string suture is a simple technique to reduce wound size and to achieve complete or partial closure of skin defects.Objective: To classify situations in which purse-string sutures can be utilized and to assess the long-term size reduction and cosmetic outcome of the final scar.Methods: Patients (93 from Severance hospital and 12 from Gangnam Severance hospital) in whom purse-string sutures were used between January 2015 and December 2019 were retrospectively reviewed.Wound site, final reconstruction method, repair duration, final wound size, and Vancouver scar scale were assessed.Results: A total of 105 patients were reviewed.Lesions were located on the trunk (48 [45.7%]), limbs (32 [30.5%]), and face (25 [23.8%]).Mean ratio of wound length/primary defect length was 0.79±0.30.Multilayered purse-string suture showed the shortest duration from excision to final repair (p<0.001) and most effectively minimized the scar size (scar to defect size ratio 0.67±0.23,p=0.002).The average Vancouver scar scale measured at the latest followup visit at least 6 months postoperatively was 1.62, and the risk of hypertrophic scarring was 8.6%.There was no significant difference in the Vancouver scar scale and the risk of hypertrophic scarring between the different surgical method groups.Conclusion: Purse-string sutures can be utilized in many stages of reconstruction to effectively reduce scar size without compromising the final cosmetic outcome.
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 | 0.001 | 0.002 |
| 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 | 0.000 |
| 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".