om sequence generation, allocation concealment, blinding, incomplete outcome data, selective outcome reporting, and other sources of bias.^\[[@R18]\]^ Disagreement was resolved by the third author. The quality of evidence of outcomes was judged according to the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) ^\[[@R19]\]^ criteria. The 2 authors independently evaluated 5 factors (risk of bias, inconsistency, indirectness, imprecision, and publication bias) that may downgrade the quality level of evidence. The recommendation level of evidence was classified into 4 categories: high, moderate, low, or very low.^\[[@R19]\]^ High-quality: further research is very unlikely to change our confidence in the estimate of effect; moderate quality: further research is likely to have an important impact on our confidence in the estimate of effect and may change the estimate; low quality: further research is very likely to have an important impact on our confidence in the estimate of effect and is likely to change the estimate; very low quality: we are very uncertain about the estimate.

2.4. Statistical analysis
-------------------------

All calculations were made using RevMan 5.3 software. Mean difference (MD) with a 95% CI was calculated for continuous data. Odds ratio (OR) or risk difference (RD) with 95% CI was calculated for dichotomous data. Heterogeneity among studies was estimated using the *I*^2^ statistic; substantial heterogeneity was represented by *I*^2^ \> 50%. A fixed-effects model was used if the heterogeneity test did not reveal significance (*I*^2^ \< 50%; *P* \> .1). Otherwise, we adopted the random-effects model. *P* \< .05 was considered significant. Sensitivity analysis was performed to explore the impact of an individual study by deleting one study each time.

3. Results
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3.1. Search results
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The initial search yielded 102 citations, of which 91 were excluded owing to duplication. After screening the titles and abstracts and readin