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Increased understanding of discrepancies between current professional CAM categorization and user descriptions may be crucial to improve communication and collaboration between CAM users and their providers.
A theoretical understanding of this discrepancy remains elusive, and it is frequently described as surprises and/or technical difficulties in the literature.
The current understanding of this discrepancy is difficult since mouse and human resistin, nevertheless, exhibit common arthritogenic [14] and atherogenic [33], [34] features even if they are produced by different cells.
Better understanding of this discrepancy could uncover hidden potential for public health intervention.
These findings could offer an understanding of the discrepancies between numerical results calculated based on the uniaxial mechanical properties and experimental results under field loading conditions.
This may provide better understanding of the discrepancies between the two data systems.
A comprehensive understanding of map discrepancies is required to improve their effectiveness in carbon accounting and local decision-making.
A combination of map differences and FNI index values provided additional spatial and quantitative understanding of map discrepancies (Fig. 4; Table 2).
We quantify the degree and spatial patterns of differences to gain an improved understanding of map discrepancies and their impacts on carbon accounting.
The necessity of better understanding of informant discrepancies in child mental health has been called for by several researchers.
A combination of map differences and FNI index values provided additional spatial and quantitative understanding of map discrepancies (Fig. 4; Table 2).
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