Comparison of the therapeutic effect of microneedling with carbon dioxide laser in hypertrophic burn scars: a randomized clinical trial
Bibliographic record
Abstract
Background: Microneedling is recently used to treat skin scars mostly atrophic scars; however, there are limited data about its effectiveness on hypertrophic burn scars. Carbon dioxide (CO2) laser is an effective method for the treatment of burn scars. Here, we aim to compare the efficacy of microneedling to CO2 laser in the treatment of hypertrophic burn scars in a randomized clinical trial. Methods: Patients with second and third-degree burn scars (n=60) were randomized to receive 3 sessions of microneedling (n=30) or CO2 laser (n=30), 4-6 weeks apart. The outcomes, including physical characteristics of the scar scored by Vancouver Scar Scale (VSS) and patients’ satisfaction with the treatment measured by Visual Analogue Scale (VAS), were investigated at baseline, at the end of the treatment period, and at the 3-month follow-up. Results: The VSS score at the follow-up visit showed a significant reduction from 6.63±1.95 to 3.8±2.3 in the microneedling group and from 7.1+2.3 to 5.6±1.7 in the CO2 laser group; while, the reduced VSS score was significantly higher in the microneedling group (P<0.05), especially in reducing the thickness (P=0.001) and pliability (P=0.001) scores. The patients’ subjective assessments for acne improvement were significantly more satisfactory in the microneedling group (P=0.025). Conclusion: Microneedling seems to be an effective method to improve hypertrophic burn scars. It also causes better scores in the physical characteristics of scar and the patients’ satisfaction compared to the CO2 laser at the 3-month follow-up.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".