Increased Patient Compliance with Silicone Gel Sheeting and Topical Silicone Gel for Hypertrophic Scar Improves Scar Outcomes
Bibliographic record
Abstract
Background: Although silicone-based products are widely used for hypertrophic scar (HS) treatment, limited research exists on the correlation between patient compliance of silicone products and scar outcomes. This study aims to investigate whether continuous and consistent use of topical silicone gel (TSG) and silicone gel sheet (SGS) improves scar characteristics and whether patient compliance influences scar outcomes.Methods: A total of 79 patients with HS were randomized into either TSG (n=38) or SGS group (n=41) by an independent physician who had not seen the patients. Patient compliance was assessed based on application frequency and duration. Patients were divided into three subgroups according to compliance. Scar characteristics were evaluated using the Vancouver Scar Scale (VSS) and patient self-assessment via a visual analogue scale (VAS) for 6 months.Results: VSS pigmentation and scar height worsened in patients who applied silicone products for less than 3 days per week, especially in the TSG group. Patients who applied silicone products for more than 4 days per week showed significant improvements in all factors. Patients reported improvements in VAS scar height, pigmentation, and hardness as application time increased. Pain and itching improved regardless of application time.Conclusion: Continuous and consistent use of TSG and SGS improved HS outcomes. Applying silicone products more than 4 days in a week correlated with better scar characteristics, except pain and itching. Applying silicone products less than 3 days in a week is not recommended, as this may worsen scar height and pigmentation.
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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 | 0.002 |
| 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".