[From financial to scientific fraud : methods to detect discrepancies in the medical literature].
Fraud is as old as Mankind. There are an enormous number of historical documents which show the interaction between truth and untruth; therefore it is not really surprising that the prevalence of publication discrepancies is increasing. More surprising is that new cases especially in the medical field generate such a huge astonishment. In financial mathematics a statistical tool for detection of fraud is known which uses the knowledge of Newcomb and Benford regarding the distribution of natural numbers. This distribution is not equal and lower numbers are more likely to be detected compared to higher ones. In this investigation all numbers contained in the blinded abstracts of the 2009 annual meeting of the Swiss Society of Anesthesia and Resuscitation (SGAR) were recorded and analyzed regarding the distribution. A manipulated abstract was also included in the investigation. The χ(2)-test was used to determine statistical differences between expected and observed counts of numbers. There was also a faked abstract integrated in the investigation. A p<0.05 was considered significant. The distribution of the 1,800 numbers in the 77 submitted abstracts followed Benford's law. The manipulated abstract was detected by statistical means (difference in expected versus observed p<0.05). Statistics cannot prove whether the content is true or not but can give some serious hints to look into the details in such conspicuous material. These are the first results of a test for the distribution of numbers presented in medical research.
Institut für Anästhesie, Chirurgische Intensivmedizin, Rettungsmedizin und Schmerztherapie, Kantonsspital, 6000, Luzern 16, Schweiz.
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G
Schüpfer
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J
Hein
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M
Casutt
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L
Steiner
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C
Konrad
D000465:Algorithms; D000776:Anesthesiology / Q000592:standards; D000818:Animals; D001777:Blood Coagulation; D001780:Blood Coagulation Tests; D003627:Data Interpretation, Statistical; D006801:Humans; D011643:Publishing / Q000592:standards; D015871:Scientific Misconduct / Q000706:statistics & numerical data*; D013552:Swine