Prevention of Thyroidectomy Scars in Asian Adults With Low-Level Light Therapy
Bibliographic record
Abstract
BACKGROUND: Abnormal wound-healing after thyroidectomy with a resulting scar is a common dermatologic consultation. Despite many medical and surgical approaches, prevention of postoperative scars is challenging. OBJECTIVE: This study validated the efficacy and safety of low-level light therapy (LLLT) using an 830/590 nm light-emitting diode (LED)-based device for prevention of thyroidectomy scars. METHODS AND MATERIALS: Thirty-five patients with linear surgical suture lines after thyroidectomy were treated with 830/590 nm LED-LLLT. Daily application of 60 J/cm (11 minutes) for 1 week starting on postoperative day 1 was followed by treatment 3 times per week for 3 additional weeks. The control group (n = 15) remained untreated. Scar-prevention effects were evaluated 1 and 3 months after thyroidectomy with colorimetric evaluation using a tristimulus-color analyzer. The Vancouver Scar Scale (VSS) score, global assessment, and a subjective satisfaction score (range: 1-4) were also determined. RESULTS: Lightness (L*) and chrome values (a*) decreased significantly at the 3-month follow-up visit in the treatment group compared with those of controls. The average VSS and GAS scores were lower in the treatment group, whereas the subjective score was not significantly different. CONCLUSION: Light-emitting diode based LLLT treatment suppressed the formation of scars after thyroidectomy and could be safely used without noticeable adverse effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".