Comparison of the Effectiveness of Nonablative Fractional Laser versus Pulsed-Dye Laser in Thyroidectomy Scar Prevention
Bibliographic record
Abstract
BACKGROUND: The anterior neck is the site of open thyroidectomy and where postoperative scarring can cause distress to patients. Both fractional and pulsed-dye lasers are effective and safe methods for preventing and improving surgical scars. OBJECTIVE: This study evaluated the improvement in scar appearance with laser intervention during the wound healing process. We evaluated the effect of nonablative fractional and pulsed-dye lasers on fresh thyroidectomy scars. METHODS: Patients were treated 3 times at 4-week interval with a follow-up visit at the 6(th) month. Scars were divided into 2 halves for each optional treatment. At every visit, a questionnaire evaluating the scar and patient satisfaction was completed. RESULTS: Thirty patients completed the 6-month process. The mean Vancouver Scar Scale scores improved significantly from 8.0 to 4.6 and 8.2 to 4.7 with nonablative fractional and pulsed-dye lasers, respectively (p<0.001). However, there was no significant difference between the 2 methods (p=0.840). CONCLUSION: There remains no consensus on the optimal treatment of scars. The present study indicates nonablative fractional and pulsed-dye lasers significantly improve scars. Nonablative fractional lasers are non-inferior to pulsed-dye lasers. Further studies are required to corroborate this finding.
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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) | 0.000 | 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".