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A bibliometric analysis of systematic reviews and meta-analyses in ophthalmology, from 2000 to 2020

Citation

Xiao, Wei et al. (2022), A bibliometric analysis of systematic reviews and meta-analyses in ophthalmology, from 2000 to 2020, Dryad, Dataset, https://doi.org/10.5061/dryad.fxpnvx0vw

Abstract

Objectives: To establish the scientometric landscape of systematic reviews and meta-analyses (SRMAs) published in the field of ophthalmology over time.

Results: A total of 2,660 SRMAs were included, and the average annual growth rate was 21.26%. China and the USA were the most productive countries, while Singapore was the country with the highest average citations per document. Wong TY was not only the most productive, but also the most frequently cited author. The most productive affiliation was National University of Singapore (n=236). SRMA output in most subspecialties had steadily increased with retina/vitreous (n=986), glaucoma (n=411) and cornea/external diseases (n=303) constantly as the most dominant fields. Rates of pre-registration and guideline compliance had dramatically increased over time, with 20.0% and 63.5% of SRMA being pre-registered and reported guideline in 2020, respectively. However, SRMAs published on ophthalmic journals tended to be less frequently pre-registered and guideline complied than those on non-ophthalmic journals (both P < 0.05).

Conclusions: The annual output of SRMAs has been rapidly increasing over the past two decades. China and the USA were the most productive countries, whereas Singapore had the most prolific and influential scholar and institution. Raising awareness and implementation of SRMA pre-registration and guideline compliance is still necessary to ensure quality, especially for ophthalmic journals.

Methods

We retrieved relevant ophthalmic SRMAs and the corresponding bibliometric parameters during 2000 to 2020 from Web of Science Core Collection. Bibliometric analysis was performed using bibliometrix package. Pre-registration and guideline compliance of each SRMA was independently assessed by two investigators.

Funding

National Natural Science Foundation of China, Award: 81670887

Natural Science Foundation of Guangdong Province, Award: 2017A030313613

Natural Science Foundation of Guangdong Province, Award: 2016A030310230

Pearl River Nova Program of Guangzhou, Award: 201806010167