Comparison of the effect of silicone gel sheets by thickness on excisional scars in pediatric and adolescent patients
Bibliographic record
Abstract
Background Selecting effective products among the various types of silicone gel sheets can be challenging for surgeons. Therefore, we assessed the effect of silicone gel sheet thickness on surgical scars in pediatric and adolescent patients.Methods From December 2017 to May 2018, we identified patients aged 1–19 years who underwent excision. Among these patients, those who were prescribed 0.3-mm or 1.0-mm-thick silicone sheets were selected. Scars were subjectively evaluated using a questionnaire consisting of seven items. Objective evaluation was performed by two plastic surgeons using the Vancouver Scar Scale (VSS).Results The mean age of the 49 selected patients was 9.78 years. The patients were divided into two groups according to the thickness of the silicone gel sheet used (0.3 mm vs. 1.0 mm). Objective evaluation of the patients’ scars revealed more favorable results in the 0.3 mm group than in the 1.0 mm group (P=0.010). Multivariate analysis of VSS scores indicated that the resulting scars in cases involving the trunk were of poorer quality than those involving facial areas (P=0.015). Additionally, favorable (i.e., below-average) VSS scores were significantly less likely in patients with longer scars (odds ratio, 0.896; 95% confidence interval, 0.834–0.963; P=0.003) or thicker silicone sheets (odds ratio, 0.085; 95% confidence interval, 0.011–0.699; P=0.019).Conclusions The use of thinner silicone gel sheets in children and adolescents resulted in better scars according to subjective evaluations, underscoring the importance of compliance in pediatric patients. The type of operation and surgical lesion should also be considered when planning the management of surgical scars.
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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 | 0.004 |
| 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".