Comparative effect of topical silicone gel and topical tretinoin cream for the prevention of hypertrophic scar and keloid formation and the improvement of scars
Bibliographic record
Abstract
BACKGROUND: Numerous modalities have been used to treat keloids and hypertrophic scars; however, optimal treatment has not yet been established. Therefore, prevention is the mainstay. Recently, silicone gel and tretinoin cream have been shown to be useful for the prevention of hypertrophic scars and keloids. However, there has been no comparative study of the two topical agents thus far. OBJECTIVE: To determine and compare the effectiveness of silicone gel and tretinoin cream for the prevention of hypertrophic scars and keloids resulting from postoperative wounds and for scar improvement. METHOD: This study included 26 patients with 44 different wounds. The postoperative wounds were divided into two treatment groups and one control group. The patients in the first and second treatment group applied silicone gel and tretinoin cream, respectively, twice a day on their wounds after their stitches were removed. In contrast, the control group patients did not apply anything. We used the Modified Vancouver Scar Scale to quantitatively examine the effectiveness of silicone gel and tretinoin cream just after stitches removal, and at 4, 8, 12 and 24 weeks after removal of the stitches. RESULTS: The silicone gel and tretinoin cream effectively prevented hypertrophic scars and keloids and improved scar effects in the two treatment groups compared with those in the control group. However, no significant difference was noted between the two treatment groups. CONCLUSION: To prevent hypertrophic scars and keloids and improve scars after surgery, application of a silicone gel or a tretinoin cream to the wounds is needed.
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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) | 0.001 | 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 | 0.000 | 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".