Prevention of Thyroidectomy Scars in Asian Adults Using a 532-nm Potassium Titanyl Phosphate Laser
Bibliographic record
Abstract
BACKGROUND: Prediction of whether postoperative wound healing will result in a hypertrophic scar or keloid is difficult. Diverse treatment options have been developed in an effort to prevent excessive scar formation. OBJECTIVE: To evaluate the efficacy and safety of a 532-nm potassium titanyl phosphate (KTP) laser in the prevention of scar formation after total thyroidectomy. MATERIALS AND METHODS: Twenty-eight individuals with Fitzpatrick skin types IV and V and linear surgical suture lines after total thyroidectomy by the same surgeon were enrolled. Twenty participants were treated using a 532-nm KTP laser two times at 2-week intervals. Eight participants were assigned to the control group. The Vancouver Scar Scale (VSS), global assessment score (GAS), and participants' subjective satisfaction were used to determine the effect of scar prevention. These results were compared with those of the control group. RESULTS: The average VSS score was remarkably lower in the KTP laser treatment group. Average GASs indicated better cosmetic outcomes in the treatment group. Participant satisfaction was also higher in the treatment group. No significant side effects were observed during follow-up. CONCLUSION: A 532-nm KTP laser can be used safely and efficiently on Asian skin to reduce scar formation after thyroidectomy. The authors have indicated no significant interest with commercial supporters.
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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) | 0.001 | 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".