Improvement in scar appearance with the usage of silicone gel containing vitamin C for pediatric Asian patients
Bibliographic record
Abstract
Background Silicone gel has been introduced as a preventive measure for scarring, yet there is limited objective evidence supporting its effectiveness in the healing of pediatric traumatic scars. This study aimed to evaluate the impact of silicone gel enriched with vitamin C on facial scars in Asian pediatric patients.Methods Pediatric patients aged 3 months to 12 years who underwent debridement and primary repair for simple facial lacerations were included in this study. A topical silicone gel mixture containing vitamin C was applied from the time of stitch removal until 6 months post-operation. Scars were evaluated at baseline, 1-, 3-, and 6-month post-application using a simplified version of the Vancouver Scar Scale, which assessed vascularity, pigmentation, and height. Scar color and pigmentation were quantified using a spectrophotometer, with comparisons to the symmetrical area on the opposite side of the scar. Statistical analysis was conducted using the Student t-test and repeated-measures analysis of variance, with post hoc testing for pairwise comparisons.Results Of the participants, 33 were men, and 19 were women. By 6 months, there was a significant improvement in the scar score on the Vancouver Scar Scale across all parameters. The erythema index showed a statistically significant decrease at each timeline (P<0.001). Similarly, the melanin index demonstrated a significant difference between the baseline and 6 months (P<0.001).Conclusions The topical application of silicone gel containing vitamin C significantly improved the appearance of fine surgical scars on the face in Asian pediatric patients.
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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".