Exploratory analysis of text duplication in peer-review reveals peer-review fraud and paper mills

Metadata analysis of retracted fake papers in Naunyn-Schmiedeberg's Archives of Pharmacology

An increasing fake paper problem is a cause for concern in the scientific community. These papers look scientific but contain manipulated data or are completely fictitious. So-called paper mills produce fake papers on a large scale and publish them in the name of people who buy authorship. The aim of this study was to learn more about the characteristics of fake papers at the metadata level. We also investigated whether some of these characteristics could be used to detect fake papers. For that purpose, we examined metadata of 12 fake papers that were retracted by Naunyn-Schmiedeberg's Archives of Pharmacology (NSAP) in recent years. We also compared many of these metadata with those of a reference group of 733 articles published by NSAP. It turned out that in many characteristics the fake papers we examined did not differ substantially from the other articles. It was only noticeable that the fake papers came almost exclusively from a certain country, used non-institutional email addresses more often than average, and referenced dubious literature significantly more often. However, these three features are only of limited use in identifying fake papers. We were also able to show that fake papers not only contaminate the scientific record while they are unidentified but also continue to do so even after retraction. Our results indicate that fake papers are well made and resemble honest papers even at the metadata level. Because they contaminate the scientific record in the long term and this cannot be fully contained even by their retraction, it is particularly important to identify them before publication. Further research on the topic of fake papers is therefore urgently needed.

Current and future applications of artificial intelligence in surgery: implications for clinical practice and research

Surgeons are skilled at making complex decisions over invasive procedures that can save lives and alleviate pain and avoid complications in patients. The knowledge to make these decisions is accumulated over years of schooling and practice. Their experience is in turn shared with others, also via peer-reviewed articles, which get published in larger and larger amounts every year. In this work, we review the literature related to the use of Artificial Intelligence (AI) in surgery. We focus on what is currently available and what is likely to come in the near future in both clinical care and research. We show that AI has the potential to be a key tool to elevate the effectiveness of training and decision-making in surgery and the discovery of relevant and valid scientific knowledge in the surgical domain. We also address concerns about AI technology, including the inability for users to interpret algorithms as well as incorrect predictions. A better understanding of AI will allow surgeons to use new tools wisely for the benefit of their patients.

Post-publication research integrity concerns in randomized clinical trials: A scoping review of the literature

BACKGROUND

Post-publication handling of integrity concerns in randomized clinical trials (RCTs) is a contentious matter.

OBJECTIVES

We undertook a scoping systematic review to map the literature regarding post-publication integrity issues in RCTs.

SEARCH STRATEGY AND SELECTION CRITERIA

Following prospective registration (https://osf.io/pgxd8) we initially searched PubMed and Scopus but subsequently extended it to include the Cochrane Library, and Google Scholar databases without language, article type or publication time restriction until November 2022. Reviewers independently selected published articles covering any aspect of post-publication research integrity concerns in RCTs.

DATA COLLECTION AND ANALYSIS

The study findings grouped within domains relating to issues concerning post-publication integrity were extracted in duplicate, verified by a third reviewer, and then tabulated.

MAIN RESULTS

The initial search captured 3159 citations, of which 89 studies were included in the review. Cross-sectional studies constituted the majority of included studies (n = 34, 38.2%), followed by systematic reviews (n = 10, 11.2%), methodology reviews/studies (n = 9, 10.1%) and other types of descriptive studies (n = 8, 9.0%). A total of 21 articles (23.6%) covered the domain on general issues, 25 (28.1%) in the journal's instructions and policies domain, eight (9.0%) in the editorial and peer review domain, one (1.1%) in the correspondence and complaints (post-publication peer review) domain, 12 (13.5%) in the investigation for concerns domain, six (6.7%) in the post-investigation decisions and sanctions domain, none in the critical appraisal guidance domain, five (5.6%) in the integrity assessment in systematic reviews domain, and 26 (29.2%) in the recommendations for future research domain. A total of 12 of the selected articles (13.5%) covered two (n = 9) or three (n = 3) different domains.

CONCLUSIONS

Various research integrity domains and issues covering post-publication aspects of RCT integrity were captured and gaps were identified, mostly related with the necessary implications for all stakeholders to improve research transparency. There is an urgent need for a multistakeholder consensus towards creating specific statements for addressing post-publication integrity concerns in RCTs.

Fake paper identification in the pool of withdrawn and rejected manuscripts submitted to Naunyn-Schmiedeberg's Archives of Pharmacology

Honesty of publications is fundamental in science. Unfortunately, science has an increasing fake paper problem with multiple cases having surfaced in recent years, even in renowned journals. There are companies, the so-called paper mills, which professionally fake research data and papers. However, there is no easy way to systematically identify these papers. Here, we show that scanning for exchanged authors in resubmissions is a simple approach to detect potential fake papers. We investigated 2056 withdrawn or rejected submissions to Naunyn-Schmiedeberg's Archives of Pharmacology (NSAP), 952 of which were subsequently published in other journals. In six cases, the stated authors of the final publications differed by more than two thirds from those named in the submission to NSAP. In four cases, they differed completely. Our results reveal that paper mills take advantage of the fact that journals are unaware of submissions to other journals. Consequently, papers can be submitted multiple times (even simultaneously), and authors can be replaced if they withdraw from their purchased authorship. We suggest that publishers collaborate with each other by sharing titles, authors, and abstracts of their submissions. Doing so would allow the detection of suspicious changes in the authorship of submitted and already published papers. Independently of such collaboration across publishers, every scientific journal can make an important contribution to the integrity of the scientific record by analyzing its own pool of withdrawn and rejected papers versus published papers according to the simple algorithm proposed in the present paper.

Detection of fake papers in the era of artificial intelligence

OBJECTIVES

Paper mills, companies that write scientific papers and gain acceptance for them, then sell authorships of these papers, present a key challenge in medicine and other healthcare fields. This challenge is becoming more acute with artificial intelligence (AI), where AI writes the manuscripts and then the paper mills sell the authorships of these papers. The aim of the current research is to provide a method for detecting fake papers.

METHODS

The method reported in this article uses a machine learning approach to create decision trees to identify fake papers. The data were collected from Web of Science and multiple journals in various fields.

RESULTS

The article presents a method to identify fake papers based on the results of decision trees. Use of this method in a case study indicated its effectiveness in identifying a fake paper.

CONCLUSIONS

This method to identify fake papers is applicable for authors, editors, and publishers across fields to investigate a single paper or to conduct an analysis of a group of manuscripts. Clinicians and others can use this method to evaluate articles they find in a search to ensure they are not fake articles and instead report actual research that was peer reviewed prior to publication in a journal.