Prophylactic low‐level light therapy for the treatment of hypertrophic scars and keloids: A case series
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Hypertrophic and keloid scars result from alterations in the wound healing process. Treating abnormal scars remains an important challenge. The aim of this case series was to investigate the effectiveness of near infrared (NIR) light emitting diode (LED) treatment as a prophylactic method to alter the wound healing process in order to avoid or attenuate the formation of hypertrophic scars or keloids. STUDY DESIGN/PATIENTS AND METHODS: Three patients (age 27-57) of phototypes I-III with hypertrophic scars or keloids due to acne or surgery participated in this case series. Following scar revision by surgery or CO(2) laser ablation on bilateral areas, one scar was treated daily by the patient at home with non-thermal, non-ablative NIR LED (805 nm at 30 mW/cm(2)) for 30 days. Efficacy assessments, conducted up to a year post-treatment, included the Vancouver Scar scale (VSS), clinical global assessment of digital photographs, and quantitative profilometry analysis using PRIMOS. Safety was documented by adverse effects monitoring. RESULTS: Significant improvements on the NIR-treated versus the control scar were seen in all efficacy measures. No significant treatment-related adverse effects were reported. CONCLUSION: Possible mechanisms involved are inhibition of TGF-beta I expression. Further studies in larger group of patients are needed to evaluate this promising technique.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".