提取模板
肌肉骨骼疾病(肩、膝、腰背)患者就医经历系统性综述的数据提取模板。

基本信息
资源简介
提取模板
【数据集背景】
本数据集源站标题为《Extraction template.》。源站首次发布:2024-10-25。
【数据内容】
源站原始描述:Musculoskeletal (MSK) conditions, particularly shoulders, knees, and the low back issues, place a significant burden on individuals, society, and healthcare systems. There is a lack of attention to negative health effects impacting patients because of their interactions to access appropriate diagnostics, assessments, and treatments. This scoping review intends to search and synthesize peer-reviewed evidence on the negative health impacts associated with navigating the healthcare system for MSK c…
【数据结构与技术规格】
文件数 1 个,合计 5.63 KB。Table 3.xls:application/vnd.ms-excel,5.63 KB。数据集 DOI:10.1371/journal.pone.0311970.t003。
【主题与分类】
源站学科分类:Medicine、Environmental Sciences not elsewhere classified、Biological Sciences not elsewhere classified、Science Policy。主题标签:findings also aim、access appropriate diagnostics、xlink "> musculoskeletal、scoping review results、scoping review intends、negative health impacts、malley &# 8217、low back issues、scoping review、health impacts。
【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/Extraction_template_/27306003
使用许可:CC BY 4.0(https://creativecommons.org/licenses/by/4.0/)。
规范引用:Jessiman-Perreault, Geneviève; Campbell, Paige; Henley, Dawn; Tribo, Danika; Kania-Richmond, Ania; Eubank, Breda H. F. (2024). Extraction template.. 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:Extraction template.
- DOI:10.1371/journal.pone.0311970.t003
- 发布日期:2024-10-25
- 文件数:1 个
- 文件总体积:5.63 KB
- 源站页面:https://plos.figshare.com/articles/dataset/Extraction_template_/27306003
命令行下载
curl -L -o "Table 3.xls" "https://ndownloader.figshare.com/files/50003106"
文件清单
Table 3.xls(5.63 KB)
文件总体积 5.63 KB(源站 API 实测)。
数据加载示例(图像类)
from PIL import Image
import glob, os
files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)
# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path) # 要求 子目录=类别
目录组织与标注格式以源站说明和下载后实际文件为准。
数据说明
Musculoskeletal (MSK) conditions, particularly shoulders, knees, and the low back issues, place a significant burden on individuals, society, and healthcare systems. There is a lack of attention to negative health effects impacting patients because of their interactions to access appropriate diagnostics, assessments, and treatments. This scoping review intends to search and synthesize peer-reviewed evidence on the negative health impacts associated with navigating the healthcare system for MSK care. A scoping review will be conducted following the PRISMA guidelines for Scoping Reviews and Arksey and O’Malley’s 5-step process. Six databases will be searched with no time or geographic limits. Included articles must meet all the following criteria: 1) the patients must be adults, 2) patients must be seeking care for their knee, low-back, or shoulder condition, 3) interacted with the healthcare system, and 4) experienced health impacts due to navigating the healthcare system. Information from each article will be charted in a pre-determined extraction. This protocol aims to share our methods ahead of analysis to increase rigour and transparency. The scoping review results will better elucidate the health impacts of the inaccessibility of high-quality care for MSK conditions. The findings also aim to inform the development of patient-centered outcomes to evaluate alterations to the current MSK pathways.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0311970.t003(source: https://plos.figshare.com/articles/dataset/Extraction_template_/27306003)。
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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