Lasers and Intense Pulsed Light for the Treatment of Pathological Scars: A Network Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Laser and intense pulsed light (IPL) therapies have shown promising effects on pathological scars, but the comparative effectiveness of laser and IPL therapies has not yet been studied. OBJECTIVES: The aim of this study was to compare and rank the efficacy of laser and IPL therapies to determine the most effective treatment method for pathological scars. METHODS: Relevant studies published up to February 2022 were identified by searching PubMed, Web of Science, Cochrane Library, CNKI, and Wanfang databases. We defined Vancouver Scar Scale score as the primary outcome. Both frequentist and Bayesian approaches were used to perform a network meta-analysis. RESULTS: We included 25 trials with a total of 1688 participants. The rankings based on the surface under the cumulative ranking curve for the Vancouver Scar Scale score based on the Bayesian approach suggested IPL + CO2 (96.43%) > pulsed dye laser (PDL) + 1064-nm Nd:YAG (yttrium aluminum garnet) laser (86.21%) > PDL + CO2 (82.15%) > CO2 (58.97%) > 1064-nm Nd:YAG (57.03%) > PDL (52%) > 532-nm Nd:YAG (33.28%) > Er:YAG + IPL (28.38%) > Er:YAG (26.56%) > IPL (15.03%) > control (13.97%). The ranking results based on the frequentist approach were basically consistent with those based on the Bayesian approach. CONCLUSIONS: The results of the network meta-analysis showed that the combination of IPL and CO2 laser has the highest probability of being the most effective intervention. However, our conclusions must be interpreted with caution due to the relatively few evaluation indicators included in our study. Future well-designed randomized controlled trials with large sample sizes are required to confirm our conclusions.
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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 | 0.025 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.043 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".