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Therefore, the literature does not demonstrate consensus regarding whether subjective or objective measures provide higher estimates of lymphedema prevalence, or the most valid.
There is inconsistency in the existing literature as to whether subjective or objective measures provide higher prevalence estimates [ 10, 11, 15].
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These revolutionary measures provided high numbers of health personnel in a short time and relieved the shortage of medical professionals to some extent.
Moreover, personalized therapeutic measures may provide higher efficacy and efficiency, which, in turn, could result in additional benefits for the patients as well as a positive cost impact on the health care system.
However, most display clinical quality measures rather than patient safety measures and provide high level data to executives rather than individual level specific data for performance management [ 9].
The concomitant use of both objective and subjective measures will, therefore, provide higher reliability and reveal more reasons of nonadherence, even in patients with high levels of adherence, and is currently recommended [ 55].
Given the complexities underlying transmission, it is likely that ferret transmission studies, as a holistic measure of transmission, provide higher sensitivity and specificity than initial screens.
At present, several MRI studies suggest that volume measures of the entorhinal cortex provide higher sensitivity than volume measures of the hippocampus when it comes to detecting AD in an early phase of the disease (Detoledo-Morrell et al. 2004; Dickerson et al. 2001; Pennanen et al. 2004).
Binding isotope effects are easily measured and provide high-resolution and detailed information on the atomic changes resulting from ligand macromolecular interactions.
The measures provide a high quality, structured evaluation that can inform clinical decision making, but the tools selected are also relevant and applicable to research projects.
The ordinal measures provide a high level of robustness against dust on eyeglasses, partial occlusions, and sensor noise; however, like all filter-based methods, the recognition accuracy depends on the degree to which muscular structures are visible in the texture.
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