Prevention of Hypertrophic Scars and Keloids by the Prophylactic Use of Topical Silicone Gel Sheets Following a Surgical Procedure in an Office Setting
Bibliographic record
Abstract
BACKGROUND Topical silicone gel sheeting has been used for more than 20 years to help reduce the size of hypertrophic scars and keloids. Its clinical efficacy and safety is well established. OBJECTIVE To determine whether topical silicone gel sheeting can be used to prevent hypertrophic scars and keloids from forming following dermatologic skin surgery. METHODS Patients undergoing skin surgery were stratified into two groups: those with no history of abnormal scarring (low-risk group) and those with a history of abnormal scarring (high-risk group). Following the procedure, patients within each group were randomized to receive either routine postoperative care or topical silicone gel sheeting (48 hours after surgery). Patients were followed for 6 months. RESULTS In the low-risk group, there were no statistical differences between individuals using routine postoperative care or using topical silicone gel sheets. In the high-risk group, there was a statistical difference (39% versus 71%) between patients who did not develop abnormal scars and used topical silicone gel sheeting and patients who developed abnormal scars after routine postoperative treatment. Those individuals having a scar revision procedure also showed a statistical difference if topical silicone gel sheeting was used following surgery. CONCLUSION Topical silicone gel sheeting, with a 20-year history of satisfaction in dermatology, now appears to be useful in the prevention of hypertrophic scars and keloids in patients undergoing scar revision.
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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) | 0.002 | 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".