在线教育材料和大型语言模型的可读性,可理解性和质量逆行环咽肌功能障碍。

IF 2.2
Nurullah Türe, Emel Tahir, Necati Enver
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引用次数: 0

摘要

目的:本研究旨在通过比较大型语言模型(LLM)生成的内容的可读性、可理解性和质量来评估在线逆行环咽功能障碍(RCPD)患者教育材料。方法:在2024年12月进行网络检索,评估了51个在线资源和4个llm (ChatGPT 4.0, Gemini 1.5 Flash, Perplexity GPT-3.5, DeepSeek-V2.5)。使用Readable分析可读性。使用PEMAT评估可理解性和可操作性,使用DISCERN评估信息质量。结果:在线材料的平均可读性水平和法学硕士的回答在11 -12年级的水平。llm的Flesch Reading Ease得分最低,尤其是DeepSeek-V2.5模型(24.21)。尽管在线(82%)和法学硕士(79%)的PEMAT可理解性得分足够,但所有组的可操作性都很低(25-37%)。DISCERN分析显示,这两种信息来源在支持治疗决策方面质量有限。结论:本研究显示在线和llm生成的RCPD材料均超过推荐的可读性水平。虽然这些材料表现出可接受的可理解性,但它们表现出低可操作性和整体质量不足,强调需要更多以患者为中心的数字健康沟通。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Readability, understandability, and quality of online education materials and large language models for retrograde cricopharyngeal muscle dysfunction.

Objective: This study aims to evaluate online patient education materials on retrograde cricopharyngeal dysfunction (RCPD) by comparing the readability, understandability, and quality of content generated by large language models (LLM).

Method: A web search in December 2024 evaluated 51 online resources and four LLMs (ChatGPT 4.0, Gemini 1.5 Flash, Perplexity GPT-3.5, DeepSeek-V2.5). Readability was analyzed using Readable.io, understandability actionability was assessed using PEMAT, and information quality was assessed using DISCERN.

Results: The average readability level of the online material and the LLM responses was at the 11th-12th grade level. The Flesch Reading Ease score was lowest for the LLMs, especially for the DeepSeek-V2.5 model (24.21). While PEMAT understandability scores were adequate for online (82%) and LLMs (79%), actionability was low across all groups (25-37%). DISCERN analyses showed that both sources of information were of limited quality in supporting treatment decisions.

Conclusion: This study revealed that both online and LLM-generated materials on RCPD exceeded the recommended readability levels. Although the materials demonstrated acceptable understandability, they exhibited low actionability and inadequate overall quality, emphasizing the need for more patient-centered digital health communication.

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