Clinical Application of Self-Adherent Scar Care Silicone Sheet and Silicone Gel in Postoperative Scar Management
Bibliographic record
Abstract
Background: Hypertrophic scars and keloids result from burns, trauma, infection, and surgery and affect daily life. Although various scar management options are available, their efficacy remains uncertain. Silicone-based products, including sheets and gels, are the primary choices for scar management. This study assesses the effectiveness of Mepiform and Mepiform Ultra Scar Gel and their optimal use.Methods: Eighteen patients who underwent primary repair between January and June 2021 were enrolled and divided into those using both Mepiform products and those using only Mepiform Ultra Scar Gel. Scars were evaluated at baseline and after 2, 4, 8, 12, and 24 weeks. The Vancouver Scar Scale score was evaluated at 12 and 24 weeks. The patients provided feedback through a survey.Results: Group 1 (both Mepiform products) showed greater improvements in vascularity (33%), height (33%), and overall sum (67%) on the Vancouver Scar Scale from weeks 12 to 24. Group 2 (only Mepiform Ultra Scar Gel) showed improvements in vascularity (22%), height (22%), and overall sum (33%). Both groups reported positive outcomes, with group 1 demonstrating higher improvement percentages for most parameters.Conclusion: This study provides valuable insights into Mepiform and Mepiform Ultra Scar Gel in postoperative scar management despite a limited sample size. All scars in the study either remained stable or improved. Better results from group 1 suggest combining Mepiform products offers advantages. The consistent and prolonged use of silicone-based products is emphasized, and larger-scale research is needed to validate these findings.
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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 | 0.000 |
| 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) | 0.002 | 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".