使用随机模型研究生物力学和人体运动的案例在同行评审期刊上产生定性证据

D. Shavelson
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引用次数: 0

摘要

生物力学和人体运动的循证实践(EBP)(以下简称BHM)由于缺乏同行评审的、高水平的、有效的和临床适用的证据而受到束缚,这抑制了在临床上使用Sackett的循证医学模型(EBM)的能力[1-3]。目前EBM的期刊研究协议遵循确定性模型。在这个模型中,有一个或两个因变量需要研究,同时所有可能影响结果的自变量都被算法或统计公式消除或控制。确定性模型具有较高的内部测试者效率(同一测试者反复得到相同的结果)和内部测试者效率(不同测试者反复得到相同的结果),从而被认为是高水平的模型。由于在研究BHM时存在大量的自变量[4],高水平的研究被迫以投诉为导向(仅限于体征或症状,如疼痛、损伤或畸形[5]),从而降低了其在个案基础上具有临床适用性的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Case for Researching Biomechanics and Human Movement Using a Stochastic Model in a Peer Reviewed Journal to Generate Qualitative Evidence
The Evidence Based Practice (EBP) of biomechanics and human movement (hereafter BHM) has been handcuffed by a lack of peer reviewed, high level, valid and clinically applicable evidence that has dampened the ability to use Sackett’s model of Evidence Based Medicine (EBM) clinically [1-3]. Current journal research protocols for EBM follow a deterministic model. In this model there are one or two dependent variables to be investigated and simultaneously all independent variables that could dampen the results are eliminated or controlled with an algorithm or statistical formulae. The deterministic model has both a high intratester (the same tester repeatedly gets the same results) and intertester (different testers repeatedly get the same results) efficiency a for it to be deemed high level. Because of the vast numbers of independent variables that exist when researching BHM [4], high level research has been forced to be complaint oriented (limited to signs or symptoms such as pain, an injury or a deformity [5]reducing its ability to have clinical applicability on a case to case basis.
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