Laser Applications in Wound and Scar Management Post-Mohs Micrographic Surgery: A Systematic Review
Bibliographic record
Abstract
Mohs micrographic surgery (MMS) can lead to complications such as scarring and delayed wound healing, particularly in sensitive areas such as the face, neck, and chest. This study aims to assess the evidence regarding the use of lasers post-MMS for wound healing and scar revision. A comprehensive systematic review of the literature was performed using databases including MEDLINE, PubMed, EMBASE, Web of Science, Cochrane Library, and CINAHL from inception until July 25, 2022. A total of 2147 unique studies were identified, from which 17 were included in the analysis. A total of 17 studies reported applications of lasers with favourable efficacy including wound healing (n = 1), resurfacing of full-thickness skin grafts and split-thickness skin grafts (n = 4), periscar telangiectasias (n = 1), functional scar contractures (n = 2), and scar texture (n = 9). Minimal adverse effects were reported with the use of lasers post-MMS. Overall, the use of lasers post-MMS is a safe and well-tolerated option for scar revision with high patient satisfaction and is less invasive than surgical interventions.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".