A 6-Year Experience of Laser Treatments for Burn Scars in a Regional Burn Center—Safety, Efficacy, and Quality Improvement
Bibliographic record
Abstract
Laser treatments have long been used as a treatment method for burn scars. Since 2012, more than 1800 laser treatments were performed at Lehigh Valley Health Network Burn Center, far exceeding any previous cohort in studies exploring laser treatments for burn scars. Although previous research has looked at improving scar appearance and physiology with laser treatments, very few have focused on safety. The purpose of the study was to determine whether laser treatments are a safe treatment option for burn scars. Four hundred and fourteen patients who had undergone at least one laser treatment in the outpatient burn center since 2012 were analyzed. Electronic medical records (EPIC) were reviewed. The data were entered in REDCap and later exported to Microsoft Excel and R Studio for statistical analysis. Most of the complications found were related to the moderate sedation during the procedures and were mild, ie, nausea. The most common adverse effect was prolonged recovery time, which can affect practice flow. The overall postoperative complication rate for laser treatments with and without moderate sedation was minimal at 2.2% and 1.4%, respectively. Pain during and after the procedure averaged 3.9 and 1.7, respectively, on a 1 to 10 scale. The Vancouver Scar Scale showed modest improvement in scar appearance over time with an average improvement of 1.4. The high variability of the Vancouver Scar Scale observed in this series underlines its lack of sensitivity. The study results show that laser treatments for burn scars in the outpatient setting generally are safe for patients in need of burn scar intervention. Some practice flow adjustments need to be taken into consideration when offering these procedures in an outpatient setting.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".