Combination of 1064 nm Long-Pulsed and Q-Switched Nd:YAG Laser for Facial Hypertrophic Scar and Hyperpigmentation Following Burn Injury
Bibliographic record
Abstract
Abstract: Burn injury is a common type of trauma which causes significant morbidity and mortality. Wound healing following burns can be complicated by the formation of hypertrophic scars and the occurrence of post-inflammatory hyperpigmentation (PIH). Neodymium:yttrium aluminum garnet (Nd:YAG) laser might become one of the treatments of choice for hypertrophic scars and PIH. We report a case of post-burn hypertrophic scars and hyperpigmentation in a 20-year-old man. The patient was consulted from the Department of Plastic Surgery and Reconstruction after scar revision. He was treated with 1064 nm long-pulsed Nd:YAG laser therapy, spot size 6 mm, fluence 55 J/cm 2 , pulse duration 3 millisecond (ms), for three sessions with one month interval, followed by 1064 nm Q-switched (QS) Nd:YAG, spot size 4 mm, fluence 2.5 J/cm 2 , frequency 2 Hz for two sessions with one month interval. Clinical improvement was observed after five sessions, characterized by scar thinning as assessed using Vancouver scar scale and increased skin tone brightness also reduced redness assessed using a spectrophotometer with no significant side effects. The management of post-burn facial scars and hyperpigmentation remains a challenge. Aside from surgery, the treatment strategy for hypertrophic scar is laser, one of which is the long-pulsed Nd:YAG laser which reduces the production of collagen. In hyperpigmented lesions, QS Nd:YAG laser destructs the melanosome. Combination of 1064 nm long-pulsed and QS Nd:YAG laser therapy provide significant improvement. These therapeutic strategies can be considered as a treatment option for post-burn hypertrophic scars and hyperpigmentation. Keywords: hypertrophic scars, Nd:YAG laser, post-inflammatory hyperpigmentation
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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 | 0.000 |
| 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".