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A lasting impact of this work has been awareness of the underlying drivers of the high false positive rate in clinical medicine and biomedical research, and efforts by journals and scientists to mitigate them. Ioannidis restated these drivers in 2016 as being: [16] Solo, siloed investigator limited to small sample sizes
John P. A. Ioannidis (/ ˌ iː ə ˈ n iː d ɪ s / EE-ə-NEE-diss; Greek: Ιωάννης Ιωαννίδης, pronounced [i.oˈanis i.oaˈniðis]; born August 21, 1965) is a Greek-American physician-scientist, writer and Stanford University professor who has made contributions to evidence-based medicine, epidemiology, and clinical research.
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Ioannidis (2005): "Why Most Published Research Findings Are False".[1]The replication crisis [a] is an ongoing methodological crisis in which the results of many scientific studies are difficult or impossible to reproduce.
The papers introducing the ranking have been quoted extensively by authors working in Bibliometrics and Scientometrics.For example, reference [3] describing an update to the methodology of this index number is cited [12] from authors publishing in journals such as SAGE's Research on Social Work Practice, [10] Elsevier's Perspectives in Ecology and Conservation, [13] Springer's Forensic Science ...
John Ioannidis argues that "claimed research findings may often be simply accurate measures of the prevailing bias." [46] He lists the following factors as those that make a paper with a positive result more likely to enter the literature and suppress negative-result papers: The studies conducted in a field have small sample sizes.
Journal of Medical Biochemistry: Biochemistry: Walter de Gruyter: English: 1982–present Journal of Medical Biography: Medical Personnel: SAGE Publishing: English: 1993–present Journal of Medical Case Reports: Medicine: BioMed Central: English: 2007–present Journal of Medical Economics: Medicine: Taylor and Francis Group: English: 1998 ...
John Ioannidis (2005), "Why Most Published Research Findings Are False" [6] In 1966, an early meta-research paper examined the statistical methods of 295 papers published in ten high-profile medical journals. It found that, "in almost 73% of the reports read ... conclusions were drawn when the justification for these conclusions was invalid."