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Types, limitations, and possible alternatives of peer review based on the literature and surgeons’ opinions via Twitter: a narrative review
Author(s) -
Sameh Hany Emile,
Hytham K. S. Hamid,
Semra Demirli Atıcı,
Doga Nur Kosker,
Mario Virgilio Papa,
Hossam Elfeki,
Chee Yang Tan,
Alaa ElHussuna,
Steven D. Wexner
Publication year - 2022
Publication title -
science editing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.354
H-Index - 11
eISSN - 2288-8063
pISSN - 2288-7474
DOI - 10.6087/kcse.257
Subject(s) - computer science , social media , incentive , process (computing) , narrative , narrative review , peer review , protocol (science) , world wide web , internet privacy , psychology , medicine , political science , alternative medicine , linguistics , philosophy , pathology , law , economics , psychotherapist , microeconomics , operating system
This review aimed to illustrate the types, limitations, and possible alternatives of peer review (PR) based on a literature review together with the opinions of a social media audience via Twitter. This study was conducted via the #OpenSourceResearch collaborative platform and combined a comprehensive literature search on the current PR system with the opinions of a social media audience of surgeons who are actively engaged in the current PR system. Six independent researchers conducted a literature search of electronic databases in addition to Google Scholar. Electronic polls were organized via Twitter to assess surgeons’ opinions on the current PR system and potential alternative approaches. PR can be classified into single-blind, double-blind, triple-blind, and open PR. Newer PR systems include interactive platforms, prepublication and postpublication commenting or review, transparent review, and collaborative review. The main limitations of the current PR system are its allegedly time-consuming nature and inconsistent, biased, and non-transparent results. Suggestions to improve the PR process include employing an interactive, double-blind PR system, using artificial intelligence to recruit reviewers, providing incentives for reviewers, and using PR templates. The above results offer several concepts for possible alternative approaches and modifications to this critically important process.

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