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This article discusses some of the epidemiologic issues in interpreting these studies.
We will consider these issues in interpreting the SUCRA rankings.
Thus, future studies will need to consider such psychometric issues in interpreting findings in young children.
Key issues in interpreting the results of the mouse studies, which are more fully described in Table 2, are reviewed.
For each of the outcomes, we assess the three critical issues in interpreting epidemiologic studies (random error, selection or measurement bias, and confounding); issues related to all reviewed outcomes (e.g., publication bias or biologic plausibility) are considered together at the end of the article.
However, researchers and public health practitioners face ethical issues in interpreting exposure results when health and safety data are not available for the pollutants under study or when there is no scientific consensus about the risks associated with exposures or efficacy of exposure reduction strategies.
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But this potential bias still remains an issue in interpreting the findings of the macroeconometric literature on ALMPs.
However, correlations between these domains remain an issue in interpreting force spectroscopy data, particularly during protein folding.
One issue in interpreting these data is the level of variability for the measures used in these studies: excessive variability can obscure treatment-related effects, or conversely, small but statistically significant changes could be viewed as treatment related, when they might in fact be within the normal range.
There is, however, still an issue in interpreting hypercolumns with respect to ocular dominance columns (ODCs).
Unfortunately, the limitation posed by the range of exposure concentrations is a widespread issue in interpreting toxicology studies of air pollution and not only studies of combined exposures.
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