Preliminary evaluation of the therapeutic effectiveness of silicone pressure dressing (Gelzone) in the prevention of post-burn scarring
Bibliographic record
Abstract
Objective: To conduct a preliminary evaluation of the effectiveness of silicone pressure dressing (Gelzone) in preventing post-burn scars.Subjects and methods: A prospective descriptive, uncontrolled clinical trial was carried out on 30 patients with post-burn sequelae scars of the extremities who presented at the Center for Plastic and Reconstructive Surgery - Le Huu Trac National Burn Hospital, between August 2024 and August 2025.Results: Most patients were female (66.7%), with a mean age of 29.2 ± 13.8 years. Thermal injury accounted for the majority of burn causes (86.7%). Scars were predominantly located in the lower extremities (76.7%), especially on the legs (56.7%). Hypertrophic scars were the most common type (73.3%), with 76.7% of patients experiencing both pain and pruritus. The mean VAS pain score significantly decreased from 6.8 ± 1.5 before treatment to 1.3 ± 1.4 at 6 months (p < 0.001). Vancouver Scar Scale parameters, including pigmentation, vascularity, pliability, and height, showed statistically significant improvement after treatment, particularly at 3 and 6 months (p < 0.001).Conclusion: Gelzone silicone pressure dressing demonstrated promising effectiveness in improving pain and clinical characteristics of post-burn scars. This method appears to be safe and effective, warranting broader clinical application and further investigation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".