European journal of clinical investigation
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Eur. J. Clin. Invest. · May 2020
Meta AnalysisAssociation between Helicobacter pylori infection and Guillain-Barré Syndrome: A meta-analysis.
Helicobacter pylori (H pylori) is a Gram-negative bacterium, considered to trigger autoimmune gastrointestinal disorders. This pathogen has also been linked to the autoimmune sequelae in extra-gastrointestinal diseases and peripheral neuropathies. Guillain-Barré syndrome (GBS) is a serious autoimmune demyelinating disorder of peripheral nerves, usually with a post-infectious onset. About 30% of cases of GBS attributed to by Campylobacter jejuni, so, H pylori, could be also involved. Growing evidence suggests the likely involvement of H pylori infection in the development of GBS. The aim of the current study was to therefore estimate the prevalence of H pylori antibodies in GBS. ⋯ The present meta-analysis showed a strong association between GBS and the presence of H pylori antibodies, especially in CSF, thereby suggesting a role of H pylori infection in the pathophysiology of GBS.
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Eur. J. Clin. Invest. · May 2020
EditorialRedefining significance and reproducibility for medical research: A plea for higher P-value thresholds for diagnostic and prognostic models.
The role of P-values for null hypothesis testing is under debate. We aim to explore the impact of the significance threshold on estimates for the strengths of associations ("effects") and the implications for different types of epidemiological research. We consider situations with normal distribution of a true effect, while varying the effect size. ⋯ We conclude that a lower P-value threshold for declaring statistical significance implies more exaggeration in an estimated effect. This implies that if a low threshold is used, effect size estimation should not be attempted, for example in the context of selecting promising discoveries that need further validation. Confirmatory studies, such as randomized controlled trials, might stick to the 0.05 threshold if adequately powered, while prediction modelling studies should use an even higher threshold, such as 0.2, to avoid strongly biased effect estimates.