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估计可理解性的临床重要最小差异的进展:众包感知实验
Kaila L Stipancic1, Frits van Brenk1, Mengyang Qiu2
1Department of Communicative Disorders and Sciences, University at Buffalo, New York.
Journal of speech, language, and hearing research : JSLHR
|October 25, 2024
概括
这项研究估计了患有多发性硬化症 (MS) 和帕金森氏症的个体对语音可理解性的最小临床重要差异 (MCID).
科学领域:
- 语音语言病理学 语言病理学
- 神经学 神经学
- 生物统计学 生物统计学
背景情况:
- 评估语音可理解性对于患有多发性硬化症 (MS) 和帕金森病 (PD) 等神经系统疾病的人来说至关重要.
- 建立最小临床重要差异 (MCID) 提供了语音可理解性有意义变化的基准.
- 目前评估可理解性变化的方法可能缺乏对临床相关差异的敏感性.
研究的目的:
- 在对照演讲者和因MS和PD而患有脱节症的演讲者中估计句子可理解性的MCID.
- 为了验证一种新的实验范式,使用众包感知数据进行MCID估计.
- 确定语音可理解性的重要变化值.
主要方法:
- 从控制演讲者和MS和PD患者在各种说话条件下录制的音频句子.
- 众包非专家听众使用对对比和全球变化评级 (GROC) 量表来评价可理解性.
- 听众评分与客观可理解性得分相关,并分析了接收器操作特征 (ROC) 曲线.
主要成果:
- 来自ROC曲线的MCID是不有效的.
- 可理解性差异的平均大小为MCID提供了有效的值.
- 对可理解性的MCID被确定为小差异约为7%,大差异约为15%.
结论:
- 该研究展示了一种收集众包感知数据以估计MCID的可行方法.
- 研究结果支持开发临床工具,将可理解性变化分类为"没有差异"",有点差异"和"有很大的差异".
- 这项研究是朝着一种通用语言迈出的一步,用于评估神经疾病和语音治疗中的可理解性变化.
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