ReviewDer Anaesthesist2017
Application of Benford's law: a valuable tool for detecting scientific papers with fabricated data? : A case study using proven falsified articles against a comparison group.
Review in Der Anaesthesist, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
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.
Who cites it
6 citing papers in PubMed.
- Can I trust this paper?Psychonomic bulletin & review · 2025Review
- Tools of the data detective: A review of statistical methods to detect data and result anomalies in psychology.Theory & psychology · 2025Article
- Trustworthiness assessment of published clinical trials: Literature review of domains and questions.Cochrane evidence synthesis and methods · 2024Review
- Investigating and preventing scientific misconduct using Benford's Law.Research integrity and peer review · 2023Article
- Benford's law and metabolomics: A tale of numbers and blood.Transfusion and apheresis science : official journal of the World Apheresis Association : official journal of the European Society for Haemapheresis · 2020Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn naturally occurring numbers the frequencies of digits 1-9 in the leading position are counterintuitively distributed because the frequencies of occurrence are unequal. Benford-Newcomb's law describes the expected distribution of these frequencies. It was previously shown that known fraudulent articles consistently violated this law.
objectiveTo compare the features of 12 known fraudulent articles from a single Japanese author to the features of 13 articles in the same research field from other Japanese authors, published during the same time period and identified with a Medline database search.
resultsAll 25 articles were assessed to determine whether the data violated the law. Formulas provided by the law were used to determine the frequencies of occurrence for the first two leading digits in manually extracted numbers. It was found that all the known fraudulent papers violated the law and 6 of the 13 articles used for comparison followed the law. Assuming that the articles in the comparison group were not falsified or fabricated, the sensitivity of assessing articles with Benford-Newcomb's law was 100% (95% confidence interval CI: 73.54-100%) but the specificity was only 46.15% (95% CI: 19.22-74.87%) and the positive predictive value was 63.16% (95% CI: 38.36-83.71%).
conclusionAll 12 of the known falsified articles violated Benford-Newcomb's law, which indicated that this analysis had a high sensitivity. The low specificity of the assessment may be explained by the assumptions made about the articles identified for comparison. Violations of Benford-Newcomb's law about the frequencies of the leading digits cannot serve as proof of falsification but they may provide a basis for deeper discussions between the editor and author about a submitted work.
Indexed as
Identifiers
28653153What OpenQuestion holds
Registered trials
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.