Very Early Pulsed Dye Laser Intervention for Optimal Cosmetic Outcome in Post-Thyroidectomy Scars
Bibliographic record
Abstract
Yul Hee Kim,1 Han-Seul Kim,1 Hyeung Kyoo Kim,2 Soo Young Kim,2 Jeonghun Lee,2 You Chan Kim,1 Young Joon Park1 1Department of Dermatology, Ajou University School of Medicine, Suwon, Republic of Korea; 2Department of Surgery, Ajou University School of Medicine, Suwon, Republic of KoreaCorrespondence: Young Joon Park, Department of Dermatology, Ajou University Hospital, 164, World Cup-ro, Yeongtong-gu, Suwon-si, Gyeonggi-Do, Suwon, Republic of Korea, Tel +82-31-219-5187, Email aro@ajou.ac.krPurpose: Early intervention of surgical scars with a pulsed dye laser is known to effectively prevent pathologic scars. Despite multiple reports on the effectiveness of the treatment, very few studies have demonstrated its appropriate initiation timing. In this study, our objective was to determine the optimal timing for initiating laser treatment following thyroidectomy.Methods: This study retrospectively analyzed 91 patients undergoing pulsed dye laser treatment post-thyroidectomy, grouping them by treatment initiation timing. The patients underwent treatment at intervals of 3– 4 weeks with at least five sessions. Those with a high pliability score were injected with intralesional corticosteroids. The Antera 3D® skin imaging analyzer was used to assess biophysical parameters.Results: The total Vancouver Scar Scale score significantly reduced after treatment in all groups. The Vancouver Scar Scale score reduction rate was significantly higher after treatment in the group for which the treatment was initiated within 3 weeks of surgery. The pigmentation and erythema score analyzed by Antera 3D® was also lower in this group.Conclusion: Early intervention using a pulsed dye laser within 3 weeks of thyroidectomy can substantially inhibit pathological scar development, providing physicians with a guide for optimal treatment commencement.Keywords: hypertrophic scars, laser treatment of scar, scar, pulsed dye laser
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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".