Prevention of Thyroidectomy Scars in Asian Adults With Low-Level Light Therapy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it