Burn Scar Revision With Tissue Expansion, Long-Pulsed 1064-nm Neodymium-Doped Yttrium Aluminum Garnet (Nd:YAG) Laser, and Microneedling: A Case Report
Bibliographic record
Abstract
Therapeutic heat devices can cause iatrogenic burns that lead to infection, grafting, and disfiguring scars. A 56-year-old woman sustained full-thickness burns of the left flank/posterior trunk after postoperative application of microwaved saline hot packs. The wounds became infected and necrotic, requiring antibiotics, debridement, and split-thickness skin grafting. Two years later, she presented with a hypertrophic, hyperpigmented ~20×10 cm graft scar and contour deformity. We implemented a staged plan: placement of two tissue expanders with serial fills over ~8 weeks; expander removal with complete scar excision and tension-free closure using the expanded native skin; adjunct scar therapies including multiple sessions of long-pulsed 1064-nm neodymium-doped yttrium aluminium garnet (Nd:YAG) laser and microneedling; and a focused office scar-revision excision (6×3 cm) for residual margin hypertrophy. Objective assessment was done with the Modified Vancouver Scar Scale (MVSS), total score improved from ~10 (pigment 3, vascularity 2, pliability 3, height 2) to ~3 at 23 months (pigment 1, vascularity 1, pliability 1, height 0). No expander-related complications occurred. This case shows that for extensive trunk graft scars, staged tissue expansion provides like-for-like, sensate skin to restore contour, while 1064-nm laser and microneedling further improve scar vascularity, thickness, pliability, and color with low morbidity.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| 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".