Plagiarism in submitted manuscripts: incidence, characteristics and optimization of screening—case study in a major specialty medical journal | Research Integrity and Peer Review | Full Text
Plagiarism in submitted manuscripts: incidence, characteristics and optimization of screening—case study in a major specialty medical journal
Janet R. HigginsEmail authorView ORCID ID profile, Feng-Chang Lin and James P. Evans
Research Integrity and Peer Review20161:13
DOI: 10.1186/s41073-016-0021-8© The Author(s) 2016
Received: 9 August 2016Accepted: 23 September 2016Published: 10 October 2016
Open Peer Review reports
Plagiarism is common and threatens the integrity of the scientific literature. However, its detection is time consuming and difficult, presenting challenges to editors and publishers who are entrusted with ensuring the integrity of published literature.
In this study, the extent of plagiarism in manuscripts submitted to a major specialty medical journal was documented. We manually curated submitted manuscripts and deemed an article contained plagiarism if one sentence had 80 % of the words copied from another published paper. Commercial plagiarism detection software was utilized and its use was optimized.
In 400 consecutively submitted manuscripts, 17 % of submissions contained unacceptable levels of plagiarized material with 82 % of plagiarized manuscripts submitted from countries where English was not an official language. Using the most commonly employed commercial plagiarism detection software, sensitivity and specificity were studied with regard to the generated plagiarism score. The cutoff score maximizing both sensitivity and specificity was 15 % (sensitivity 84.8 % and specificity 80.5 %).
Plagiarism was a common occurrence among manuscripts submitted for publication to a major American specialty medical journal and most manuscripts with plagiarized material were submitted from countries in which English was not an official language. The use of commercial plagiarism detection software can be optimized by selecting a cutoff score that reflects desired sensitivity and specificity.
iThenticate Plagiarism detection Optimization
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