Application and effect of tension-reducing suture in surgical treatment of hypertrophic scar
Bibliographic record
Abstract
PURPOSE: To investigate the application and effectiveness of tension-reducing suture in the repair of hypertrophic scars. METHODS: A retrospective analysis of clinical data was conducted on 82 patients with hypertrophic scars treated at the Department of Burns and Plastic Surgery of Nanjing Drum Tower Hospital from September 2021 to December 2022. Patients were operated with combination of heart-shaped tension-reducing suturing technique and looped, broad, and deep buried (LBD) suturing technique or conventional suture method. Outcomes of surgical treatment were assessed before and 6 months after surgery using the Patient and Observer Scar Assessment Scale (POSAS) and the Vancouver Scar Scale (VSS). RESULTS: Improvements were achieved on scar quality compared to that preoperatively, with a reduction in scar width (1.7 ± 0.6 cm vs. 0.7 ± 0.2 cm, P < 0.001). Assessment using the POSAS and VSS scales showed significant improvements in each single parameter and total score compared to preoperative values (P < 0.05). The Combination method group achieved better score in total score of VSS scale, in color, stiffness, thickness and overall opinion of PSAS scale, and in vascularity, thickness, pliability and overall opinion of OSAS scale. CONCLUSION: The amalgamation of the heart-shaped tension-reducing suturing technique and the LBD suturing technique has shown promising outcomes, garnering notably high levels of patient satisfaction in the context of hypertrophic scar repair. Patients have exhibited favorable postoperative recoveries, underscoring the clinical merit and the prospective broader applicability of this approach in the realm of hypertrophic scar management.
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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 | 0.000 | 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) | 0.001 | 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".