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The methodological quality of robotic surgical meta-analyses needed to be improved: a cross-sectional study
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.
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Metadata
- Authors
- Yan, P., Yao, L., Li, H., Zhang, M., Xun, Y., Li, M., Cai, H., Lu, C., Hu, L., Guo, T., Liu, R., & Yang, K.
- Affiliations
- Gansu Provincial Hospital (CN)
- Hong Kong Baptist University (HK)
- Lanzhou University (CN)
- Chinese PLA General Hospital (CN)
- Corresponding author
- Liu, Rong
- Yang, Kehu
- ORCID
- Venue
- Journal of Clinical Epidemiology
- Publisher
- Elsevier BV
- Published
- 2019-05
- Volume
- 109
- Pages
- 20-29
- ISSN
- 0895-4356 (ISSN-L)
- OpenAlex
- W2905661211
- Cited by
- 107
- References
- 49
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 9 · 2023: 11 · 2022: 13 · 2021: 26 · 2020: 22 · 2019: 21
- FWCI
- 13.11
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Clinical Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Lanzhou University Institutional Repository (submittedVersion)
- Funders
- Fundamental Research Funds for the Central Universities, China (16LZUJBWTD013, 18LZUJBWZX006)
- Gansu Provincial Hospital, China (17GSSY9-1, Grant no. 18GSSY3-8)
- Key Laboratory of Evidence Based Medicine and Knowledge Translation Foundation (GSXZYZH2018006)
- Laboratory Of Intelligent Medical Engineering (GSXZYZH2018001)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Social Sciences › Library and Information Sciences › scientometrics and bibliometrics research
- Social Sciences › Sociology and Political Science › Delphi Technique in Research
- Keywords
- robotic surgery, meta-analysis, methodological quality, AMSTAR 2, reporting quality, research registration, funding disclosure, journal impact factor
- MeSH terms
- Cross-Sectional Studies, Humans, Meta-Analysis as Topic, Multivariate Analysis, Robotic Surgical Procedures
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