研究目的
To assess the effects of photobiomodulation therapy (PBMT) in the treatment of androgenic alopecia (AA) through a meta-analysis of randomized controlled trials (RCTs).
研究成果
PBMT seems to be a promising noninvasive treatment for AA, improving hair density and stimulating hair growth. However, due to potential limitations, more large-scale RCTs are needed to verify these findings and investigate the safety of PBMT in AA.
研究不足
Substantial heterogeneity in hair density was observed among the included studies. Some studies had a relatively small sample size, which might overestimate the treatment effects. The lack of available data prevented the assessment of treatment-related adverse events in PBMT. The dosage of PBMT, duration, and frequency were not analyzed due to limited data. All included studies were sponsored by manufacturers, potentially affecting the results due to conflict of interest and possible bias.
1:Experimental Design and Method Selection:
A meta-analysis of RCTs was conducted to assess the effects of PBMT in AA patients. The study adhered to the methods of the Cochrane Handbook for Systematic Reviews of Interventions and reported findings according to the PRISMA statement.
2:Sample Selection and Data Sources:
RCTs published in Pubmed, Web of Science, and Embase were systematically reviewed. The inclusion criteria were RCTs involving adult participants diagnosed with AA, treated with PBMT, and reporting outcome measures of hair density and hair growth.
3:List of Experimental Equipment and Materials:
Not explicitly mentioned in the abstract.
4:Experimental Procedures and Operational Workflow:
Data extraction was performed by two independent investigators using a standardized form. The risk of bias was assessed using the method recommended by Cochrane Collaboration.
5:Data Analysis Methods:
Results were expressed as weighted mean difference (WMD) with 95% confidence interval (95%CI) and a risk ratio (RR) of 95%CI. Heterogeneity was assessed using Cochrane Q chi-square test and I2 statistics. A fixed-effects model or a randomized-effects model was used based on heterogeneity.
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