Efficacy and Safety of Lasers in Treating Head and Neck Capillary Malformations: A Systematic Review and Meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: This study aims to comprehensively review and compare the safety and efficacy of commonly used lasers in the management of head and neck capillary malformations (CMs). DATA SOURCES: PubMed, Embase, Cochrane, and Web of Science. REVIEW METHODS: Databases were searched from inception to August 2023. Study protocols adhered to the PRISMA guidelines. Cohort studies reporting CM laser treatment outcomes were included. Study validity was tested using the Newcastle-Ottawa Scale. Meta-analysis was conducted using the inverse variance method and a fixed effects model, with treatment success defined as achieving 25% to 100% clearance of the lesion. RESULTS: A total of 725 studies were screened, and 14 full-text articles met the criteria, comprising 714 patients. Patients underwent a mean of 6.5 laser treatment sessions, consisting of 80% pulsed dye laser (PDL), 12% neodymium-doped yttrium-aluminum-garnet laser (Nd:YAG), and 8% 577 nm yellow laser treatments. Meta-analysis revealed an overall treatment success rate of 97% (95% confidence interval [CI]: 0.96-0.98). Subgroup analysis by laser type resulted a success rate of 96% (95% CI: 0.94-0.973) for PDL, 99% (95% CI: 0.979-1.008) for Nd:YAG laser, and 86% (95% CI: 0.7621-0.964) for 577 nm yellow laser. The complication rate by laser type was 9% (95% CI: 0.031-0.147) for PDL, 4% (95% CI: 0.007-0.080) for Nd:YAG laser, and no reported complication data for 577 nm yellow laser. CONCLUSION: The vast majority of the available data on laser treatment of head and neck CMs suggest excellent outcomes and low complication rate using PDL treatment. Centers using Nd:YAG laser reported a slightly higher rate of successful treatment with fewer complications than PDL or 577 nm yellow laser treatment.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.033 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".