Large Language Models for Stuttering Assessment and Therapy
Alignment of Large Language Model Responses on Stuttering Definition, Assessment, and Therapy With Clinical Practice Guidelines: A Multi-Model Comparison
This observational study aims to compare the quality of responses generated by four large language models (ChatGPT, Claude, Google Gemini, and Grok-4.20) to questions about stuttering. The study focuses on three main areas: general information about stuttering, clinical assessment, and therapy. A total of nine questions were developed based on evidence-based clinical practice guidance, including the American Speech-Language-Hearing Association (ASHA) Practice Portal. Each question is presented to each language model in separate sessions, and the generated responses are recorded for evaluation.
Five speech-language therapists with clinical experience in stuttering independently evaluate the model-generated responses. Each response is rated for relevance, accuracy, clarity, completeness, and consistency using a 5-point Likert scale. The responses are also compared with guideline-based reference information. The study does not involve any clinical intervention or treatment of patients. The aim is to determine how closely large language model responses align with current clinical practice guidance and to identify their potential strengths and limitations when used as informational or clinical support tools in the field of stuttering.
研究概览
地位
条件
研究类型
注册 (实际的)
联系人和位置
学习地点
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Istanbul
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Istanbul、Istanbul、土耳其（türkiye）、34310
- Istanbul Gelisim University
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参与标准
资格标准
适合学习的年龄
接受健康志愿者
有资格学习的性别
取样方法
研究人群
The study population consisted of adult speech-language therapists in Türkiye who were actively working in the field of stuttering and fluency disorders. Five speech-language therapists with clinical experience in stuttering were purposively selected to independently evaluate responses generated by four large language models to standardized questions concerning stuttering information, assessment, and therapy.
描述
Inclusion Criteria:
- Having an undergraduate or postgraduate degree in speech and language therapy.
- Actively practicing clinically in the field of stuttering/fluency disorders.
- Voluntarily agreeing to participate in the study.
- Completing the evaluation form in full.
Exclusion Criteria:
- Not having active clinical experience in the field of stuttering.
- Incomplete completion of the evaluation form.
- Withdrawal of voluntary participation during the evaluation process.
学习计划
研究是如何设计的？
设计细节
- 观测模型：OTHER
- 时间观点：CROSS_SECTIONAL
队列和干预
团体/队列 |
干预/治疗 |
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Expert Speech-Language Therapists
Five speech-language therapists with clinical experience in stuttering independently evaluated responses generated by four large language models (ChatGPT, Claude, Google Gemini, and Grok-4.20). The responses addressed nine questions covering general information about stuttering, clinical assessment, and therapy. Each response was rated for relevance, accuracy, clarity, completeness, and consistency using a 5-point Likert scale. |
ChatGPT responses to nine standardized questions on stuttering information, assessment, and therapy were generated in separate sessions and recorded for independent expert evaluation. Questions were repeated under comparable conditions to allow assessment of response consistency. Claude responses to nine standardized questions on stuttering information, assessment, and therapy were generated in separate sessions and recorded for independent expert evaluation. Questions were repeated under comparable conditions to allow assessment of response consistency. Google Gemini responses to nine standardized questions on stuttering information, assessment, and therapy were generated in separate sessions and recorded for independent expert evaluation. Questions were repeated under comparable conditions to allow assessment of response consistency. Grok-4.20 responses to nine standardized questions on stuttering information, assessment, and therapy were generated in separate sessions and recorded for independent expert evaluation. Questions were repeated under comparable conditions to allow assessment of response consistency. |
研究衡量的是什么？
主要结果指标
结果测量 |
措施说明 |
大体时间 |
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Expert-Rated Quality of Large Language Model Responses
大体时间：During the single cross-sectional expert evaluation conducted over approximately 1 month
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Responses generated by four large language models (ChatGPT, Claude, Google Gemini, and Grok-4.20) to nine standardized questions about stuttering were independently evaluated by five speech-language therapists with clinical experience in stuttering. Each response was rated using a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree) across five criteria: relevance, accuracy, clarity, completeness, and consistency. Responses were evaluated against guideline-based reference information derived from evidence-based clinical practice guidance. Higher scores indicate better response quality and closer alignment with clinical practice guidance. Mean scores were calculated for each large language model and each evaluation criterion. |
During the single cross-sectional expert evaluation conducted over approximately 1 month
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合作者和调查者
研究记录日期
研究主要日期
学习开始 (实际的)
初级完成 (实际的)
研究完成 (实际的)
研究注册日期
首次提交
首先提交符合 QC 标准的
首次发布 (实际的)
研究记录更新
最后更新发布 (实际的)
上次提交的符合 QC 标准的更新
最后验证
更多信息
此信息直接从 clinicaltrials.gov 网站检索，没有任何更改。如果您有任何更改、删除或更新研究详细信息的请求，请联系 register@clinicaltrials.gov. clinicaltrials.gov 上实施更改，我们的网站上也会自动更新.