用于数据分析的R脚本
用于比较LLM和搜索引擎在正畸问题上回答质量、共情、可读性和满意度的R分析脚本。
基本信息
资源简介
用于数据分析的R脚本
【数据集背景】
本数据集源站标题为《R script used for data analysis.》。源站首次发布：2026-01-02。
【数据内容】
源站原始描述：Background Large Language Models (LLMs) highlight their potential in supporting patient education and self-management. Their performance in responses to orthodontic questions has yet to be explored. Objectives This study aims to compare the quality, empathy, readability, and satisfaction of responses from LLMs and search engines on common orthodontic questions. Methods Forty-five common orthodontic questions (six categories) and a prompt were developed, and a self-designed multidimensional evalu…
文件 pone.0339908.s015.R 为文本内容，开头为：data <- X2025_1_10_可分析数据 View(data) data <- X2025_4_30_可分析问题平均分 View(data) data_510<-X2025_5_10_问题分组差异性分析 View(data_510) summary(data$OvSa[data$AI=="se"]…
【数据结构与技术规格】
文件数 1 个，合计 20.40 KB。pone.0339908.s015.R：text/plain，20.40 KB。数据集 DOI：10.1371/journal.pone.0339908.s015。
【主题与分类】
源站学科分类：Molecular Biology、Biotechnology、Developmental Biology、Science Policy、Infectious Diseases。主题标签：supporting patient education、appliance selection category、therapeutic outcomes category、rated significantly higher、orthodontic patient education、outperformed search engines、xlink "> forty、search engine responses、3 search engines、001 ), readability。
【适用方向】
文本类数据可用于医学自然语言处理，如命名实体识别、信息抽取与文本分类。
【获取与许可】
源站页面：https://plos.figshare.com/articles/dataset/_p_R_script_used_for_data_analysis_p_/30989130
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Ren, Yuanyuan; Sun, Jing (2026). R script used for data analysis.. PLOS ONE. Dataset
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载，适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档，适合快速预览和评估数据集质量。
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公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取（Figshare）
- 源站标题：<p>R script used for data analysis.</p>
- DOI：10.1371/journal.pone.0339908.s015
- 发布日期：2026-01-02
- 文件数：1 个
- 文件总体积：20.40 KB
- 源站页面：https://plos.figshare.com/articles/dataset/_p_R_script_used_for_data_analysis_p_/30989130
命令行下载
curl -L -o "pone.0339908.s015.R" "https://ndownloader.figshare.com/files/60753507"
文件清单
pone.0339908.s015.R（20.40 KB）
文件总体积 20.40 KB（源站 API 实测）。
数据说明
Background
Large Language Models (LLMs) highlight their potential in supporting patient education and self-management. Their performance in responses to orthodontic questions has yet to be explored.
Objectives
This study aims to compare the quality, empathy, readability, and satisfaction of responses from LLMs and search engines on common orthodontic questions.
Methods
Forty-five common orthodontic questions (six categories) and a prompt were developed, and a self-designed multidimensional evaluation questionnaire was constructed. Questions were presented to 5 LLMs and 3 search engines on December,22,2024. The primary outcomes were the median expert-rated scores of LLMs versus search engine responses on quality, empathy, readability, and satisfaction, using 5- or 10-point Likert scales.
Results
LLMs scored significantly higher than search engines in quality (4.00 vs. 3.50, p Conclusions
In this cross-sectional study, the LLMs, particularly GPT-4o, outperformed search engines. These results indicate the potential of LLMs as supplementary tools for orthodontic patient education and self-management.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0339908.s015（source: https://plos.figshare.com/articles/dataset/_p_R_script_used_for_data_analysis_p_/30989130）。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
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