Fake Publications in Biomedical Science: Red-flagging Method Indicates Mass Production

ABSTRACT Background Integrity of academic publishing is increasingly undermined by fake science publications massively produced by commercial “editing services” (so-called “paper mills”). They use AI-supported, automated production techniques at scale and sell fake publications to students, scientists, and physicians under pressure to advance their careers. Because the scale of fake publications in biomedicine is unknown, we developed a simple method to red-flag them and estimate their number. Methods To identify indicators able to red-flag fake publications (RFPs), we sent questionnaires to authors. Based on author responses, a classification rule was applied initially using the two-indicators “non-institutional email AND no international authors” (“email+NIA”) to sub-samples of 15,120 PubMed®-listed publications regarding publication date, journal, impact factor, country and RFP citations. Using the indicator “hospital affiliation” (“email+hospital”), this classification (tallying) rule was validated by comparing 400 known fakes with 400 matched presumed non-fakes. Results Two initial indicators (“email+NIA”) revealed a rapid rise of RFP from 2010 to 2020. Countries with the highest RFP proportion were Russia, Turkey, China, Egypt, India and China (39%-55%). When using the “email+hospital” tallying-rule, sensitivity of RFP identification was 86%, the false alarm rate 44%, and the estimated RFP rate in 2020 was 11.0%. Adding a RFP-citation indicator (“email+hospital+RFP-citations”) increased the sensitivity to 90% and reduced the false alarm rate to 37%. Given 1.3 million biomedical Scimago-listed publications, the estimated annual RFP number in 2020 is about 150,000. Conclusions Potential fake publications can be red-flagged using simple-to-use, validated classification rules to earmark them for subsequent scrutiny. RFP rates are increasing, suggesting higher actual fake rates than previously reported. The large scale and proliferation of fake publications in biomedicine can damage trust in science, endanger public health, and impact economic spending and security. Easy-to-apply fake detection methods, as proposed here, or more complex automated methods can enable the retraction of fake publications at scale and help prevent further damage to the permanent scientific record.