Elastic Silicone Occlusive Sheeting Versus Silicone Occlusive Sheeting in the Treatment of Scars: A Randomized Controlled Trial
Bibliographic record
Abstract
INTRODUCTION: Hypertrophic scarring caused by conventional open thyroidectomy is prevalent among Asians and published trials have proved that silicone occlusive sheeting is a useful treatment for hypertrophic scarring. However, silicone occlusive sheeting does not effectively prevent scar widening. Here, we report elastic silicone occlusive sheeting as a new type of silicone application. In this study, we compared the effects of elastic silicone occlusive sheeting on scar width and appearance after conventional open thyroidectomy with those of silicone occlusive sheeting. METHODS: In this prospective, randomized, assessor-blinded study, a total of 74 patients who underwent conventional open thyroidectomy were recruited to undergo elastic silicone occlusive sheeting and silicone occlusive sheeting on the healed wound. Split scar study and scar quality were assessed on the basis of scar width, Vancouver scar scale, pain/itching visual analogue scale, and patients' subjective degree of satisfaction with the scar, during the patients' 6-month review. RESULTS: A total of 61 patients completed the study. Scar width, Vancouver scar scale score, and patients' subjective degree of satisfaction indicated that elastic silicone occlusive sheeting was associated with narrower scars and significant improvement in scar appearance. The two methods did not differ significantly with regard to pain/itching visual analogue scale. CONCLUSIONS: Our findings highlight elastic silicone occlusive sheeting as an effective treatment for scarring, resulting in narrower and better scars after conventional open thyroidectomy. The use of elastic silicone occlusive sheeting after conventional open thyroidectomy may minimize the formation of hypertrophic scars in the early postoperative period. TRIAL REGISTRATION: ChiCTR2100049740.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.000 | 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 teacher head, 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".