Literature DB >> 30579979

The methodological quality of robotic surgical meta-analyses needed to be improved: a cross-sectional study.

Peijing Yan1, Liang Yao2, Huijuan Li3, Min Zhang4, Yangqin Xun3, Meixuan Li3, Hui Cai5, Cuncun Lu6, Lidong Hu5, Tiankang Guo5, Rong Liu7, Kehu Yang8.   

Abstract

OBJECTIVES: The aims of the article were to assess the methodological quality of robotic surgical meta-analyses (MAs) using A MeaSurement Tool to Assess systematic Reviews (AMSTAR-2) and to explore the factors of methodological quality. STUDY DESIGN AND
SETTING: Robotic surgical MAs published between 2015 and 2018 were identified through a systematical search in PubMed, EMBASE, Cochrane library, and Web of Science databases. The methodological quality of eligible MAs was evaluated by AMSTAR-2. Data extraction and the methodological quality of MAs assessment were double checked by four trained reviewers. The intraclass correlation coefficient (ICC) was used to assess the consistency of quantitative measurements, and the ICC for overall score and score of critical domains were 0.952 and 0.912, respectively. Multivariate regression analysis was used to identify potential factors affecting methodological quality.
RESULTS: A total of 123 MAs focused on 18 surgical locations were included. The findings showed that, regarding quality, only two (1.6%) of 123 MAs were high, two (1.6%) were moderate, two (1.6%) were low, and the remainder 117 (95.1%) were critical low. Multiple linear regression analysis revealed that publishing year and journal rank independently associated with methodological quality of MAs; origin region (P > 0.05), Preferred Reporting Items for Systematic Reviews and Meta-Analyses (P = 0.421), randomized controlled trial enrollment (P = 0.304), and funding support (P = 0.958) did not influence the quality of the MAs. Registration (item 2) and funding reported for individual studies (item 10) showed the poorest adherence in the MAs.
CONCLUSION: Our study showed that the previously published robotic surgical MAs lack good scientific quality, especially in those published in Q2- to Q4-rated journals. Potential solutions to improve the quality of future robotic surgical MAs include preregistration and funding reported for individual studies.
Copyright © 2018 Elsevier Inc. All rights reserved.

Keywords:  AMSTAR-2; Cross-sectional study; Meta-analyses; Methodological quality; Robotic surgical

Year:  2018        PMID: 30579979     DOI: 10.1016/j.jclinepi.2018.12.013

Source DB:  PubMed          Journal:  J Clin Epidemiol        ISSN: 0895-4356            Impact factor:   6.437


  38 in total

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