关键行动数据集
包含创伤性院内心脏骤停虚拟现实训练中定义的关键行动列表,用于医学教育评估。

基本信息
资源简介
关键行动数据集
【数据集背景】
本数据集源站标题为《Critical actions.》。源站首次发布:2025-01-24。
【数据内容】
源站原始描述:Background With the increasing availability and use of digital tools such as virtual reality in medical education, there is a need to evaluate their impact on clinical performance and decision-making among healthcare professionals. The Trauma SimVR study is investigating the efficacy of virtual reality training in the context of traumatic in-hospital cardiac arrest. Methods and analysis This study protocol (clinicaltrials.gov identifier: NCT06445764) for a single-center, prospective, randomized,…
【数据结构与技术规格】
文件数 1 个,合计 5.63 KB。Table 2.xls:application/vnd.ms-excel,5.63 KB。数据集 DOI:10.1371/journal.pone.0316828.t002。
【主题与分类】
源站学科分类:Medicine、Biological Sciences not elsewhere classified、Infectious Diseases、Virology。主题标签:provide valuable insights、guideline adherence specific、controlled trial focuses、v </ u、trauma </ u、sim </ u、r </ u、various medical specialties、trauma patient care、trauma simvr study。
【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/Critical_actions_/28275428
使用许可:CC BY 4.0(https://creativecommons.org/licenses/by/4.0/)。
规范引用:Lintschinger, Josef Michael; Metelka, Philipp; Kapral, Lorenz; Kahlfuss, Florian; Reischmann, Lena; Kaider, Alexandra; et al. (2025). Critical actions.. 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:Critical actions.
- DOI:10.1371/journal.pone.0316828.t002
- 发布日期:2025-01-24
- 文件数:1 个
- 文件总体积:5.63 KB
- 源站页面:https://plos.figshare.com/articles/dataset/Critical_actions_/28275428
命令行下载
curl -L -o "Table 2.xls" "https://ndownloader.figshare.com/files/51913994"
文件清单
Table 2.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
Background
With the increasing availability and use of digital tools such as virtual reality in medical education, there is a need to evaluate their impact on clinical performance and decision-making among healthcare professionals. The Trauma SimVR study is investigating the efficacy of virtual reality training in the context of traumatic in-hospital cardiac arrest.
Methods and analysis
This study protocol (clinicaltrials.gov identifier: NCT06445764) for a single-center, prospective, randomized, controlled trial focuses on first-year residents in anesthesiology/intensive care, traumatology, and emergency medicine. The study will compare the clinical performance in a simulated scenario between participants who received virtual reality training and those who received traditional e-learning courses for preparation. The primary endpoint is the time to a predefined intervention to treat the underlying cause of the simulated traumatic cardiac arrest. Secondary endpoints include protocol deviations, cognitive load during simulated scenarios, and the influence of gender and personality characteristics on learning outcomes. The e-learning and the virtual reality training content will be developed in collaboration with experts from various medical specialties and nursing, focusing on procedural processes, guideline adherence specific to trauma patient care, and traumatic in-hospital cardiac arrest.
Results
The results of this study will provide valuable insights into the efficacy of virtual reality training, contributing to the advancement of medical education, and serve as a foundation for future research in this rapidly evolving field.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0316828.t002(source: https://plos.figshare.com/articles/dataset/Critical_actions_/28275428)。
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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