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。

Figshare
2024-10-25 更新
浏览 27
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Extraction template

基本信息

模态
文本
大小
0.000005632 GB
许可
CC BY 4.0
创建/更新时间
2024-10-25
版本
v1

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

官方服务

专家保障全链路合规授权与标注交付

陕川双基地 × 超十家三甲医院直连

从合规授权、采集治理、专业标注到数据集交付,我们提供全链路闭环服务,让高质量临床数据即拿即用。

⚡️ 需要原始数据或标注支持?联系我们获取定制方案。

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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)

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。

⚡️ 需要数据支持或标注服务?立即联系我们获取专业方案。

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