Evidence map›Paper›PMID 40991025›Full record

ArticleNaunyn-Schmiedeberg's archives of pharmacology2026

Fake publications in biomedical science: red-flagging method indicates mass production.

Bernhard A Sabel, Emely Knaack, Gerd Gigerenzer, Mirela-Ioana Bilc

Abstract read
In one paragraph

Article in Naunyn-Schmiedeberg's archives of pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed.

  1. AI hallucinations in academic writing: implications for research integrity.Naunyn-Schmiedeberg's archives of pharmacology · 2026
    Review
  2. Review
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. The entities enabling scientific fraud at scale are large, resilient, and growing rapidly.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  10. Article
  11. Article
  12. The misalignment of incentives in academic publishing and implications for journal reform.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  13. Review
  14. Article
  15. Article
  16. The landscape of biomedical research.Patterns (New York, N.Y.) · 2024
    Article
  17. Article
  18. Article
  19. Cytidine Acetylation Across the Tree of Life.Accounts of chemical research · 2024
    Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Bernhard A SabelInstitute of Medical Psychology, Medical Faculty, Otto-Von-Guericke University of Magdeburg, Leipziger Straße 44, 39120, Magdeburg, Germany. bernhard.sabel@med.ovgu.de.
Emely KnaackInstitute of Medical Psychology, Medical Faculty, Otto-Von-Guericke University of Magdeburg, Leipziger Straße 44, 39120, Magdeburg, Germany.
Gerd GigerenzerMax-Planck Institute for Human Development, Berlin, Germany.
Mirela-Ioana BilcInstitute of Medical Psychology, Medical Faculty, Otto-Von-Guericke University of Magdeburg, Leipziger Straße 44, 39120, Magdeburg, Germany.

Funding

State of Sachsen-Anhalt Ministry of Science, Energy, Climate Protection and Environment FKZ I 167
6 · The paper itself

Abstract

Integrity of academic publishing is increasingly undermined by fake publications massively produced by commercial "editing services" (so-called "paper mills"). These services use AI-supported 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 an easy-to-apply rule to red-flag potentially fake publications and estimate their number. After analyzing questionnaires sent to authors of published papers, we developed simple classification rules and tested them in a 9-step bibliometric analysis in a sample of 17,120 publications listed in PubMed®. We first validated various simple rules and finally applied a multifactorial tallying rule comparing 400 known fakes with 400 random (presumed) non-fakes. This rule was then applied to 1,000 random publications each from 2020 and 2023. The multifactorial tallying rule was the best red-flagging method, with a 94% sensitivity and only a 11.5% false-alarm rate. The rate of red-flagged articles increased during the last decade, reaching an estimated 14.9% in 2020 and 16.3% in 2023. Countries with the highest proportion of read-flagged publications were China, India, Iran, Russia, and Turkey, with China and India the largest absolute contributors globally. Applying Bayes' rule resulted in an estimate of 5.8% actual fakes in the biomedical literature. Given 1.86 million Scimago-listed biomedical publications in 2023, we estimate the actual number of true fakes at 107.800 articles per year, growing steadily. Scientific publications in biomedicine can be red-flagged as potentially fake using fast-and-frugal classification rules to earmark them for subsequent scrutiny. When applying Bayes´rule, the annual true scale of fake publishing in biomedicine is about 19 times that of the 5.671 biomedicine retractions in 2023. This scale of fraudulent publishing is concerning as it can damage trust in science, endanger public health, and impact economic spending. But fake detection tools can enable retractions of fake publications at scale and help prevent further damage to the permanent scientific record.

Indexed as

Biomedical ResearchPeriodicals as TopicPublishingBibliometricsHumansSurveys and QuestionnairesBiomedical scienceFakePaper millResearch integrityScience fraudTrust

Identifiers

PMID40991025
PMCPMC12901079

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.