RTSS-Voice: Towards a unified system to classify treatments for muscle tension dysphonia

项目摘要 系统地改善目前的语音治疗结果是有问题的，因为具体的临床医生行动 (i.e.,成分）负责改善患者功能（即，目标不明。比如说， 比较有效性研究（CER）可以表明，治疗A在总体上优于治疗B 成果十。但是为什么A疗法能提供更好的结果呢？为什么有些患者在治疗B显著 治疗A中的一些患者在“更好”方案下保持不变 节目？需要一个基于理论的系统来科学地识别与以下因素相关的计划成分： 改善患者的预后。并且需要标准标签来制作识别的活性成分 可以在治疗项目中推广。因此，本项目将使用一个理论驱动的框架， 描述康复治疗的成分/目标-称为康复治疗 规范系统（RTSS）-和标准语音专用术语/定义-称为RTSS-语音- 标准地描述（目标1）和比较（目标2）9个众所周知的和不同的治疗变化， 声音疗法此外，我们将创建/测试一个实施工具包，以促进RTSS-语音在临床中的采用。 5个语音中心的护理（目标3）。假设RTSS和RTSS-Voice将表征所有 无需修订的治疗（目标1），并确定一种治疗所特有的成分/靶点 和/或在多种疗法中是共同的（Aim 2）。由于RTSS-Voice将帮助临床医生考虑 他们的治疗更具体，并与9个循证疗法，我们假设采用将是 与所有5个语音中心的改善结果相关。生成的互斥 在治疗方案中使用成分/靶点将明显改善CER状态， 识别/比较治疗方案中的活性成分;而不是目前的做法， 研究/比较整个程序。临床采用将产生具有标准成分的大型数据集 与结果相关联，这将促进创新假设和可解释的数据挖掘/机器学习 切实提高声音治疗的效果。这项工作可能会推广到其他中心，因为 20多名有影响力的临床医生的参与和实施工具包。

Project Summary Systematically improving upon current voice therapy outcomes is problematic as the specific clinician actions (i.e., ingredients) responsible for improved patient functioning (i.e., targets) are unknown. For example, Comparative Effectiveness Research (CER) can show that therapy A works better than therapy B on global outcome X. But why did therapy A provide better outcomes? Why did some patients in therapy B significantly improve with the “worse” program; and some patients in therapy A remain unchanged with the “better” program? A theory-based system is needed to scientifically identify a program’s ingredients associated with improved outcomes across patients. And standard labels are needed to make the identified active ingredients generalizable across therapy programs. Therefore, this project will use a theory-driven framework for describing the ingredients/targets of rehabilitation treatments—called the Rehabilitation Treatment Specification System (RTSS)—and standard voice-specific terminology/definitions—called the RTSS-Voice— to standardly describe (Aim 1) and compare (Aim 2) variations in treatment across 9 well-known and diverse voice therapies. Also, we will create/test an implementation toolkit to facilitate RTSS-Voice adoption in clinical care across 5 Voice Centers (Aim 3). It is hypothesized that the RTSS and RTSS-Voice will characterize all therapies without needing revisions (Aim 1) and identify ingredients/targets that are unique to one therapy and/or common across multiple therapies (Aim2). And since the RTSS-Voice will help clinicians think about their treatment more specifically and in relation to 9 evidence-based therapies, we hypothesize adoption will be associated with improved outcomes at all 5 Voice Centers. The resulting list of mutually exclusive ingredient/targets across therapy programs will obviously improve the state of CER by enabling the identification/comparison of active ingredients across therapy programs; instead of the current practice of studying/comparing entire programs. Clinical adoption will result in large datasets with standard ingredients linked to outcomes, which will facilitate innovative hypotheses and interpretable datamining/machine learning to realistically improve voice therapy effectiveness. This work is likely to generalize to other Centers due to the involvement of >20 influential clinicians and the implementation toolkit.