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

项目摘要 系统地改进目前的发声治疗结果是有问题的，因为特定的临床医生行动 对改善患者功能(即目标)负责的成分(即成分)尚不清楚。例如, 比较有效性研究(CER)可以表明，在全球范围内，治疗A的效果好于治疗B 结果X。但是为什么治疗A提供了更好的结果？为什么治疗B中的一些患者显著 通过“较差”计划改善；治疗A中的一些患者与“较好”计划保持不变 程序？需要一个基于理论的系统来科学地确定与以下项目相关的项目成分 改善了患者的预后。需要标准标签才能使已识别的活性成分 可在各种治疗方案中推广。因此，这个项目将使用一个理论驱动的框架来 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- 标准化地描述(目标1)和比较(目标2)9个众所周知和不同的治疗方法的变化 语音疗法。此外，我们将创建/测试一个实施工具包，以促进RTSS-Voice在临床上的采用 跨5个语音中心的关怀(目标3)。假设RTSS和RTSS-Voice将表征所有 不需要修改的疗法(目标1)，并确定一种疗法特有的成分/目标 和/或在多种疗法中通用(AIM2)。由于RTSS-Voice将帮助临床医生考虑 他们的治疗更具体，与9种循证疗法有关，我们假设采用 与改善所有5个语音中心的结果相关。得到的互斥列表 跨治疗计划的成分/靶点将通过使 识别/比较治疗方案中的有效成分；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 切实提高发声治疗效果。这项工作很可能推广到其他中心，因为 参与&gt；20名有影响力的临床医生和实施工具包。

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.