面向患者的炎症性肠病问题大语言模型回答基准评估表1
五种大语言模型对20个炎症性肠病患者问题的回答质量评估数据，涵盖信息质量、透明度和可读性指标。
基本信息
资源简介
面向患者的炎症性肠病问题大语言模型回答基准评估表1
【一句话概览】
本数据集是 figshare 公开的结构化表格数据集，收录 1 个文件、合计 51.29 KB，主题涵盖 benchmarking、generative artificial intelligence、health literacy、inflammatory bowel disease、large language models，可用于统计建模、特征工程与队列分析，CC BY 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《Table 1_Benchmarking public large language model responses to patient-facing inflammatory bowel disease questions: informational quality, transparency proxies, and readability.docx》。配套论文《Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum》发表于 JAMA Internal Medicine（2023），DOI：10.1001/jamainternmed.2023.1838。源站首次发布：2026-04-10。
【数据内容】
源站原始描述：BackgroundPatient-facing large language model (LLM) outputs for inflammatory bowel disease (IBD) must be decision-relevant, readable, and verifiable. MethodsIn a cross-sectional benchmark using a guideline-derived question set, five publicly available LLMs provided answers to 20 single-intent patient IBD questions, mapped to prespecified decision-critical domains across the care pathway (100 model–question responses). Queries were conducted from January 17–24, 2026, via official web interfaces u…
【数据结构与技术规格】
文件数 1 个，合计 51.29 KB。Table 1_Benchmarking public large language model responses to patient-facing inflammatory bowel disease questions: informational quality, transparency.docx：application/vnd.openxmlformats-officedocument.wordprocessingml.document，51.29 KB。数据集 DOI：10.3389/fpubh.2026.1810358.s001。
【主题与分类】
源站学科分类：Public Health and Health Services not elsewhere classified。主题标签：benchmarking、generative artificial intelligence、health literacy、inflammatory bowel disease、large language models、patient-facing、readability、transparency。
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Benchmarking_public_large_language_model_responses_to_patient-facing_inflammatory_bowel_disease_questions_informational_quality_transparency_proxies_and_readability_docx/31978185
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Wang, Xiaoyue; Yin, Chengguang; He, Haiyang; Guo, Jinjiao; Fu, Xueting; Bai, Feihu (2026). Table 1_Benchmarking public large language model responses to patient-facing inflammatory bowel disease questions: informational quality, transparency proxies, and readability.docx. Frontiers. Dataset
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使用方式
数据集获取（Figshare）
- 源站标题：Table 1_Benchmarking public large language model responses to patient-facing inflammatory bowel disease questions: informational quality, transparency proxies, and readability.docx
- DOI：10.3389/fpubh.2026.1810358.s001
- 发布日期：2026-04-10
- 文件数：1 个
- 文件总体积：51.29 KB
- 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Benchmarking_public_large_language_model_responses_to_patient-facing_inflammatory_bowel_disease_questions_informational_quality_transparency_proxies_and_readability_docx/31978185
命令行下载
curl -L -o "Table 1_Benchmarking public large language model responses to patient-facing inflammatory bowel disease questions: informational quality, transparency.docx" "https://ndownloader.figshare.com/files/63609021"
文件清单
Table 1_Benchmarking public large language model responses to patient-facing inflammatory bowel disease questions: informational quality, transparency.docx（51.29 KB）
文件总体积 51.29 KB（源站 API 实测）。
数据说明
BackgroundPatient-facing large language model (LLM) outputs for inflammatory bowel disease (IBD) must be decision-relevant, readable, and verifiable.
MethodsIn a cross-sectional benchmark using a guideline-derived question set, five publicly available LLMs provided answers to 20 single-intent patient IBD questions, mapped to prespecified decision-critical domains across the care pathway (100 model–question responses). Queries were conducted from January 17–24, 2026, via official web interfaces under default settings (privacy mode; new chat per prompt). Two blinded raters evaluated informational quality and completeness (using DISCERN, EQIP, and the Global Quality Scale), transparency proxies (based on JAMA benchmark criteria), and readability through the Automated Readability Index, Flesch Reading Ease, Gunning Fog Index, Flesch–Kincaid Grade Level, Coleman–Liau Index, and SMOG. Overall differences were assessed using within-question paired Friedman tests with Holm adjustment, and effect size was quantified with Kendall’'s W.
ResultsInterrater agreement was high [DISCERN ICC(A,1) = 0.842; EQIP ICC(A,1) = 0.760; GQS weighted κ = 0.812; JAMA weighted κ = 0.936]. Median DISCERN scores ranged from 43.5 to 57.5, and EQIP scores ranged from 67.5 to 77.5, while transparency remained limited (JAMA median 0–1/4). Readability consistently failed to meet patient targets, with grade-level indices exceeding sixth grade and Flesch Reading Ease medians ranging from 15 to 36 (compared to a target of ≥80 for “easy” readability). All 10 outcomes varied significantly across models (Holm-adjusted P Under default settings, publicly available LLMs exhibit variable informational quality for IBD but consistently poor transparency and readability. Patient-facing deployment should mandate provenance, currency, and disclosure fields, as well as outputs targeted to appropriate grade levels.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.3389/fpubh.2026.1810358.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Benchmarking_public_large_language_model_responses_to_patient-facing_inflammatory_bowel_disease_questions_informational_quality_transparency_proxies_and_readability_docx/31978185）。
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