Application of fractional carbon dioxide laser monotherapy in keloids: A meta‐analysis
Bibliographic record
Abstract
Abstract Background There is no evidence‐based guidance on the use of fractional CO2 laser in the excision of scars. Aim To explore the effectiveness and safety of fractional CO2 laser in the treatment of keloids. Methods In this meta‐analysis, we searched the PubMed, Embase, and Cochrane databases from inception to April 2023. We only included studies reporting fractional CO2 laser treatment of keloids. We excluded duplicate published studies, incomplete studies, those with incomplete data, animal experiments, literature reviews, and systematic studies. Results The pooled results showed that the Vancouver Scar Scale (VSS) parameters of height weighted mean difference (WMD) = −1.10, 95% confidence interval (CI): −1.46 to −0.74), pigmentation (WMD = −0.61, 95% CI: −1.00 to −0.21), and pliability (WMD = −0.90, 95% CI: −1.17 to −0.63) were significantly improved after fractional CO2 laser treatment of keloids. However, vascularity did not significantly change. Additionally, the total VSS was significantly improved after treatment (WMD = −4.01, 95% CI: −6.22 to −1.79). The Patient Scars Assessment Scale was significantly improved after treatment (WMD = −15.31, 95% CI: −18.31 to −12.31). Regarding safety, the incidences of hyperpigmentation, hypopigmentation, pain, telangiectasia, and atrophy were 5%, 0%, 11%, 2% (95% CI: 0%–6%), and 0% (95% CI: 0%–4%), respectively. Conclusions Fractional CO2 laser is effective in the treatment of keloids and can effectively improve the height, pigmentation, and pliability of scars, and patients are satisfied with this treatment. Further studies should explore the role of combination therapy.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.049 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".