Clinical efficacy of CO <sub>2</sub> fractional laser in treating post‐burn hypertrophic scars in children: A meta‐analysis
Bibliographic record
Abstract
Abstract Objective To evaluate and explore the efficacy of CO 2 fractional laser in treating post‐burn hypertrophic scars in children through Meta‐analysis. Methods English databases (PubMed, Web of Science and The National Library of Medicine), as well as Chinese databases (China National Knowledge Infrastructure and Wanfang Data) were searched. RevMan 5.3 software was used to data analysis. Results A total of 10 pieces of literature were included, involving 413 children. Meta‐analysis showed that: (1) The average Vancouver Scar Scale after surgery was significantly lower than that before surgery [weight mean difference (WMD) = −3.56, 95% confidence interval (CI):−4.53,−2.58, p < 0.001]; (2) After CO 2 fractional laser, pigmentation [WMD = −0.74, 95% CI:−1.10,−0.38, p < 0.001], pliability [WMD = −0.92, 95% CI:−1.20,−0.65, p < 0.001], vascularity [WMD = −0.77, 95% CI:−1.09,−0.46, p < 0.001], height [WMD = −0.57, 95% CI:−0.95,−0.19, p < 0.001] were improved compared with those before surgery. (3) The average Visual Analogue Scale (VAS) after surgery was significantly lower than that before surgery [WMD = −3.94, 95% CI:−5.69,−2.22, p < 0.001]. (4) Both Patient and Observer Scar Assessment Scale (POSAS)‐Observer [WMD = −3.98, 95% CI:−8.44,0.47, p < 0.001] and POSAS‐Patient [WMD = −4.98, 95% CI:−8.09,−1.87, p < 0.001] were significantly lower than those before surgery. (5) Erythema and vesicles were the most common complications after CO 2 fractional laser therapy, with an incidence of 4.09%. Conclusion CO 2 fractional laser is beneficial to the recovery of hypertrophic scar after burn in children, and can effectively improve the scar symptoms and signs in children, with desirable clinical efficacy.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.033 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".