Articles: human.
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Anesthesia and analgesia · Sep 2017
ReviewBias, Confounding, and Interaction: Lions and Tigers, and Bears, Oh My!
Epidemiologists seek to make a valid inference about the causal effect between an exposure and a disease in a specific population, using representative sample data from a specific population. Clinical researchers likewise seek to make a valid inference about the association between an intervention and outcome(s) in a specific population, based upon their randomly collected, representative sample data. Both do so by using the available data about the sample variable to make a valid estimate about its corresponding or underlying, but unknown population parameter. ⋯ Bias and confounding are common potential explanations for statistically significant associations between exposure and outcome when the true relationship is noncausal. Understanding interactions is vital to proper interpretation of treatment effects. These complex concepts should be consistently and appropriately considered whenever one is not only designing but also analyzing and interpreting data from a randomized trial or observational study.
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Yonsei medical journal · Sep 2017
Erratum to "Diagnostic Algorithm to Reflect Regressive Changes of Human Papilloma Virus in Tissue Biopsies" by Lhee MJ, et al. (Yonsei Med J 2014;55:331-338.).
This corrects the article on p. 331 in vol. 55, PMID: 24532500.
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We developed a spinal nerve root wrapping rodent model to evaluate the relationship between recombinant human bone morphogenetic protein 2 (rhBMP-2) dosage and the degree of inflammation. ⋯ 2.