优化识别2型糖尿病高风险策略的数据表

数据集获取（Figshare）

• 源站标题：DataSheet_1_Optimizing strategies to identify high risk of developing type 2 diabetes.docx
• DOI：10.3389/fendo.2023.1166147.s001
• 发布日期：2023-06-28
• 文件数：1 个
• 文件总体积：831.32 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/DataSheet_1_Optimizing_strategies_to_identify_high_risk_of_developing_type_2_diabetes_docx/23591169

命令行下载

curl -L -o "DataSheet_1_Optimizing strategies to identify high risk of developing type 2 diabetes.docx" "https://ndownloader.figshare.com/files/41384544"

文件清单

• DataSheet_1_Optimizing strategies to identify high risk of developing type 2 diabetes.docx（831.32 KB）

文件总体积 831.32 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

IntroductionThe success of diabetes prevention based on early treatment depends on high-quality screening. This study compared the diagnostic properties of currently recommended screening strategies against alternative score-based rules to identify those at high risk of developing diabetes.
MethodsThe study used data from ELSA-Brasil, a contemporary cohort followed up for a mean (standard deviation) of 7.4 (0.54) years, to develop risk functions with logistic regression to predict incident diabetes based on socioeconomic, lifestyle, clinical, and laboratory variables. We compared the predictive capacity of these functions against traditional pre-diabetes cutoffs of fasting plasma glucose (FPG), 2-h plasma glucose (2hPG), and glycated hemoglobin (HbA1c) alone or combined with recommended screening questionnaires.
ResultsPresenting FPG > 100 mg/dl predicted 76.6% of future cases of diabetes in the cohort at the cost of labeling 40.6% of the sample as high risk. If FPG testing was performed only in those with a positive American Diabetes Association (ADA) questionnaire, labeling was reduced to 12.2%, but only 33% of future cases were identified. Scores using continuously expressed clinical and laboratory variables produced a better balance between detecting more cases and labeling fewer false positives. They consistently outperformed strategies based on categorical cutoffs. For example, a score composed of both clinical and laboratory data, calibrated to detect a risk of future diabetes ≥20%, predicted 54% of future diabetes cases, labeled only 15.3% as high risk, and, compared to the FPG ≥ 100 mg/dl strategy, nearly doubled the probability of future diabetes among screen positives.
DiscussionCurrently recommended screening strategies are inferior to alternatives based on continuous clinical and laboratory variables.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fendo.2023.1166147.s001（source: https://frontiersin.figshare.com/articles/dataset/DataSheet_1_Optimizing_strategies_to_identify_high_risk_of_developing_type_2_diabetes_docx/23591169）。