Skin Wound Tension Reduction Device Combined with Ablative Fractional Carbon Dioxide Laser to Reduce Scar Formation After Excision of Pediatric Facial Skin Lesions: A Prospective Cohort Study
Bibliographic record
Abstract
Purpose: To investigate the efficacy of skin wound tension reduction device (SWTRD) combined with ablative fractional carbon dioxide laser (CO2-AFL) for the prevention of scar formation following the excision of facial cutaneous lesions in children. Methods: Patients undergoing surgical excision of facial cutaneous lesions in our hospital between May 2019 and April 2021 were enrolled. After the excision of facial cutaneous lesions and based on the personal intents and conditions, patients were assigned to undergo SWTRD combined with CO2-AFL. Outcome evaluations were as follows: defect size, incision width, scar width, the Vancouver Scar Scale (VSS) and University of North Carolina 4P Scar Scale (UNC4P). Results: A total of 25 pediatric patients (mean age, 9.88 years) were enrolled in the study. Following the treatment of SWTRD+CO2-AFL, scar widths were relatively narrow and the appearance of the incision scars was significantly improved. A significant reduction in the patient-reported UNC4P scores at 6 months (3, 1– 4) was observed when compared with that at 2 months (0, 0– 1) after surgery ( p < 0.001). A similar reduction in the VSS scar scale was also evident (6 months: 1, 0.75– 2.5 vs 2 months: 6.5– 8.5; p < 0.001). Conclusion: Combined SWTRD and CO2-AFL treatment effectively modulates the scar formation after the incision is healed and resulting in preventing scar widening, leading to the improvement of scar appearance, reduction in wound pain and pruritus and its overall prognosis. Keywords: facial cutaneous lesions, pediatric patients, skin wound tension reduction device, fractional carbon dioxide laser, scar
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".