Efficacy of fractional carbon dioxide laser therapy for burn scars: a meta-analysis
Bibliographic record
Abstract
The present study evaluates the effectiveness of fractional carbon dioxide (CO2) laser for the treatment of burn scars. Literature search was conducted in electronic databases and studies were selected by following pre-determined eligibility criteria. Random effect meta-analyses were performed to achieve the effect size of the changes (mean difference (MD) between post-treatment and pretreatment values) in selected scar assessment scale scores and other important outcome measures. 14 studies were included. Treatment of burn scars with fractional CO2 laser significantly improved Vancouver Scar Scale (MD −3.01 [95% confidence interval (CI) −3.79, −2.22]; p ˂ .00001), Patient and Observer Scar Assessment Scale (POSAS)– Patient (MD −14.38 [95% CI −17.62, −11.13]; p ˂ .00001, POSAS – Observer (MD −8.81 [9% CI −11.60, −6.02]; p ˂ .00001 and Scar Assessment Scale (MD 1.64 [95% CI 0.49, 2.78]; p = .005) scores especially with regards to pigmentation, vascularity, pliability, and height of scar. Pain and pruritis also improved with this treatment. Scar thickness measured with ultrasonography decreased non-significantly (MD −0.48 [95% CI −1.04, 0.09]; p = .1) whereas cutometer measures, R0 (scar firmness) and R2 (scar elasticity) did not change meaningfully. Fractional CO2 laser therapy is a valuable tool for the treatment of burn scars which has potential for reducing scar severity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.043 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".