EZSCAN用于未确诊2型糖尿病的系统评价与荟萃分析

数据集获取（Figshare）

• 源站标题：EZSCAN for undiagnosed type 2 diabetes mellitus: A systematic review and meta-analysis
• DOI：10.1371/journal.pone.0187297
• 发布日期：2017-10-30
• 文件数：3 个
• 文件总体积：163.84 KB
• 源站页面：https://plos.figshare.com/articles/dataset/EZSCAN_for_undiagnosed_type_2_diabetes_mellitus_A_systematic_review_and_meta-analysis/5551903

命令行下载

curl -L -o "S1 Checklist.doc" "https://ndownloader.figshare.com/files/9632410"
curl -L -o "S1 Table.doc" "https://ndownloader.figshare.com/files/9632416"
curl -L -o "S2 Table.doc" "https://ndownloader.figshare.com/files/9632428"

文件清单

• S1 Checklist.doc（65.02 KB）
• S1 Table.doc（53.25 KB）
• S2 Table.doc（45.57 KB）

文件总体积 163.84 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

Objectives
The EZSCAN is a non-invasive device that, by evaluating sweat gland function, may detect subjects with type 2 diabetes mellitus (T2DM). The aim of the study was to conduct a systematic review and meta-analysis including studies assessing the performance of the EZSCAN for detecting cases of undiagnosed T2DM.
Methodology/Principal findings
We searched for observational studies including diagnostic accuracy and performance results assessing EZSCAN for detecting cases of undiagnosed T2DM. OVID (Medline, Embase, Global Health), CINAHL and SCOPUS databases, plus secondary resources, were searched until March 29, 2017. The following keywords were utilized for the systematic searching: type 2 diabetes mellitus, hyperglycemia, EZSCAN, SUDOSCAN, and sudomotor function. Two investigators extracted the information for meta-analysis and assessed the quality of the data using the Revised Version of the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist. Pooled estimates were obtained by fitting the logistic-normal random-effects model without covariates but random intercepts and using the Freeman-Tukey Arcsine Transformation to stabilize variances. Heterogeneity was also assessed using the I2 measure. Four studies (n = 7,720) were included, three of them used oral glucose tolerance test as the gold standard. Using Hierarchical Summary Receiver Operating Characteristic model, summary sensitivity was 72.0% (95%CI: 60.0%– 83.0%), whereas specificity was 56.0% (95%CI: 38.0%– 74.0%). Studies were very heterogeneous (I2 for sensitivity: 79.2% and for specificity: 99.1%) regarding the inclusion criteria and bias was present mainly due to participants selection.
Conclusions
The sensitivity of EZSCAN for detecting cases of undiagnosed T2DM seems to be acceptable, but evidence of high heterogeneity and participant selection bias was detected in most of the studies included. More studies are needed to evaluate the performance of the EZSCAN for undiagnosed T2DM screening, especially at the population level.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0187297（source: https://plos.figshare.com/articles/dataset/EZSCAN_for_undiagnosed_type_2_diabetes_mellitus_A_systematic_review_and_meta-analysis/5551903）。