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Reply to Francescato et al.: on correct computation of confidence intervals for kinetic parameters

机译:回复Francescato等:在正确计算动力学参数的置信区间

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We thank Francescato and colleagues for their interest inour studies (Goulding et al., 2018a,b).Our decision to use the 1-sec interpolation method wasborne out of the data of Benson et al., (2017b). Theseauthors used 2*105 Monte Carlo simulations of moderate-intensity exercise transitions to determine the impactof various averaging and fitting procedures on sV_ O2estimation.A particular strength of this study was the abilityto produce a clean _V O2 kinetic trace with known (i.e.“true”) parameters. Subsequently, this trace was sampledusing simulations of breathing frequency and the additionof Gaussian noise similar to that associated with experimentallyobtained _V O2 data, but with known underlyingkinetic parameters. This study, therefore, represents theonly study which has allowed precise quantification ofboth the precision and the accuracy of _V O2 averagingmethods.
机译:我们感谢Francescato及其同事进行兴趣研究(Goulding等,2018A,B)。我们决定使用1-SEC插值方法从Benson等人的数据中脱颖而出。(2017b)。使用2 * 105蒙特卡罗模拟中等强度运动过渡的2 * 105蒙特卡罗模拟,以确定各种平均和拟合程序对SV_ O2的影响。本研究的特殊强度是产生清洁_V O2动力学迹线的能力,具有已知的(即“真实” ) 参数。随后,该迹线是对呼吸频率的采样模拟,并且高斯噪声的增加与与实验室有关的_V O2数据相关的高斯噪声,但是具有已知的下面的基因下参数。因此,这项研究代表着神的研究,它允许精确定量禁止精度和_V o2平均方法的精度和准确性。

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