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Firstly, the TST is an imperfect test and therefore the LTBI prevalence estimate might be impacted by the test performance.
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The TMT is particularly suitable for studying selective attention, as it does not require any explicit feedback by subjects, and the test performance can be evaluated automatically and reproduced by a computational model.
Heterogeneity can then be assessed by subgroup analyses of the test performance.
Consequently, we used random effects meta-analysis to incorporate the additional uncertainty caused by that heterogeneity in the test performance results for each instrument.
We intended to compare the test performance by comparing the AUCs and by comparing the specificities when the sensitivity was approximately 80%.
The quantitative statistical analysis of the two tests was conducted by analyzing the test performances of the study participants.
Overall, the test performances varied by patient population, study design and study quality.
It is our intention that this tool be used not only by researchers for generating comprehensive SV calls, but also by programmers/developers for the testing performance of tools they are developing against currently available tools.
Regarding the testing performance measured by using AUC errors, the best results have been obtained by using RFA with NMSC classifier.
We also found a significant increase in dynamic balance as verified by the functional test performance in the Pilates and Proprioceptive Neuromuscular Facilitation groups.
The clusters were obtained by treating families as single objects whose dissimilarity was measured by the pairwise test performance differences between the family members.
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