A prospective study to evaluate the treatment effect of pulsed dye laser on thyroidectomy hypertrophic scars using 3D imaging analysis
Bibliographic record
Abstract
OBJECTIVES: The pulsed dye laser (PDL) is an effective modality for preventing and improving hypertrophic scars (HSs). However, the heterogeneity of the parameter settings of the laser and subjective scar assessment methods used in most studies resulting in uncertainty with treatment plans. Therefore, we investigated the treatment effect of the PDL (V-beam; Candela Laser Corporation) on HSs in post-thyroidectomy patients using three-dimensional imaging analysis and intended to provide a systemic and optimal treatment protocol. METHODS: ) at 4- to 6-week intervals. Patients with an elevated lesion also received intralesional corticosteroid (ICS) treatment. After every two treatment sessions, we assessed the patients' HS using the Vancouver Scar Scale (VSS), a patient satisfaction questionnaire, and with a three-dimensional (3D) skin imaging device (Antera 3D™; Miravex Limited). RESULTS: In repeated-measures analysis of variance, the mean VSS and patient satisfaction significantly improved (p < 0.001), with significant differences in these values observed until the sixth and eighth treatment sessions, respectively. In the quantitative analysis using Antera 3D™, the mean height, pigmentation, and vascularity scores were observed to be significantly improved (p < 0.001). Significant differences in these values were observed until the fourth, second, and eighth treatment sessions, respectively. Subgroup analysis according to ICS treatment showed no significant differences in scar characteristics between those with and without ICS treatment. CONCLUSIONS: In this study, we found that the PDL was effective in reducing scar height, vascularity, and pigmentation in patients with thyroidectomy HS using 3D imaging analysis. Furthermore, we have suggested a cost-effective treatment plan with the 595 nm PDL.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 0.001 |
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".