正畸疼痛阿拉伯语在线信息质量评估数据集
包含74个阿拉伯语正畸疼痛网站的链接及其质量与可读性评估分数的数据集。
基本信息
资源简介
正畸疼痛阿拉伯语在线信息质量评估数据集
【数据集背景】
本数据集源站标题为《List on included links.》。源站首次发布：2024-05-23。
【数据内容】
源站原始描述：Background This study assesses the quality and readability of Arabic online information about orthodontic pain. With the increasing reliance on the internet for health information, especially among Arabic speakers, it’s critical to ensure the accuracy and comprehensiveness of available content. Our methodology involved a systematic search using the Arabic term for (Orthodontic Pain) in Google, Bing, and Yahoo. This search yielded 193,856 results, from which 74 websites were selected based on pre…
【数据结构与技术规格】
文件数 1 个，合计 17.79 KB。pone.0303308.s001.xlsx：application/vnd.openxmlformats-officedocument.spreadsheetml.sheet，17.79 KB。数据集 DOI：10.1371/journal.pone.0303308.s001。
【主题与分类】
源站学科分类：Medicine、Cell Biology、Biotechnology、Evolutionary Biology、Environmental Sciences not elsewhere classified、Biological Sciences not elsewhere classified。主题标签：search yielded 193、median smog score、kincaid grade level、indicating middle school、american medical association、96 (&# 177、centered online information、systematic search using、four criteria simultaneously、arabic online resources。
【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://plos.figshare.com/articles/dataset/List_on_included_links_/25891656
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Alassaf, Muath Saad; Hamadallah, Hatem Hazzaa; Almuzaini, Abdulrahman; Aloufi, Aseel M.; Al-Turki, Khalid N.; Khoshhal, Ahmed S.; et al. (2024). List on included links.. 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。
免登录有偿下载
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：List on included links.
- DOI：10.1371/journal.pone.0303308.s001
- 发布日期：2024-05-23
- 文件数：1 个
- 文件总体积：17.79 KB
- 源站页面：https://plos.figshare.com/articles/dataset/List_on_included_links_/25891656
命令行下载
curl -L -o "pone.0303308.s001.xlsx" "https://ndownloader.figshare.com/files/46508211"
文件清单
pone.0303308.s001.xlsx（17.79 KB）
文件总体积 17.79 KB（源站 API 实测）。
数据说明
Background
This study assesses the quality and readability of Arabic online information about orthodontic pain. With the increasing reliance on the internet for health information, especially among Arabic speakers, it’s critical to ensure the accuracy and comprehensiveness of available content. Our methodology involved a systematic search using the Arabic term for (Orthodontic Pain) in Google, Bing, and Yahoo. This search yielded 193,856 results, from which 74 websites were selected based on predefined criteria, excluding duplicates, scientific papers, and non-Arabic content.
Materials and methods
For quality assessment, we used the DISCERN instrument, the Journal of the American Medical Association (JAMA) benchmarks, and the Health on the Net (HON) code. Readability was evaluated using the Simplified Measure of Gobbledygook (SMOG), Flesch Reading Ease Score (FRES), and Flesch-Kincaid Grade Level (FKGL) scores.
Results
Results indicated that none of the websites received the HONcode seal. The DISCERN assessment showed median total scores of 14.96 (± 5.65), with low overall quality ratings. In JAMA benchmarks, currency was the most achieved aspect, observed in 45 websites (60.81%), but none met all four criteria simultaneously. Readability scores suggested that the content was generally understandable, with a median FKGL score of 6.98 and a median SMOG score of 3.98, indicating middle school-level readability.
Conclusion
This study reveals a significant gap in the quality of Arabic online resources on orthodontic pain, highlighting the need for improved standards and reliability. Most websites failed to meet established quality criteria, underscoring the necessity for more accurate and trustworthy health information for Arabic-speaking patients.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0303308.s001（source: https://plos.figshare.com/articles/dataset/List_on_included_links_/25891656）。
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。
⚡️ 需要数据支持或标注服务？立即联系我们获取专业方案。
