Appropriate Timing for Thyroidectomy Scar Treatment Using a 1,550-nm Fractional Erbium-Glass Laser
Bibliographic record
Abstract
BACKGROUND: Surgical scarring is a common cosmetic problem that occurs in various surgical fields, including dermatology. Many trials have been conducted to determine how to prevent this distressing scar formation. A 1,550-nm fractional erbium-glass laser has been used to improve the appearance of surgical scars, but an appropriate treatment time has not been established. OBJECTIVES: To determine the appropriate time to apply 1,550-nm fractional erbium-glass laser treatment for thyroidectomy scars. MATERIALS AND METHODS: Korean patients with linear surgical suture lines after thyroidectomy (N = 65) were treated using a 1,550-nm fractional erbium-glass laser. Patients were divided into three groups according to postoperative treatment time. Laser treatment was started in 40, 15, and 10 patients 3 weeks, 3 months, and 6 months postoperatively, respectively. Each patient was treated three times at 1-month intervals using the same parameters (14 mJ, 100 spots/cm(2) , 2 passes). RESULTS: Mean Vancouver Scar Scale scores were significantly lower after laser treatment (p < .01), with the greatest difference in the group that began treatment 3 weeks postoperatively. Global assessment also indicated better cosmetic outcomes in the 3-week postoperative treatment group. CONCLUSION: Early postoperative 1,550-nm fractional erbium-glass laser treatment of thyroidectomy scars is more effective than later treatment.
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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".