动静脉瘘失败风险预测模型性能数据集

数据集获取（Figshare）

• 源站标题：Performance of models predicting.
• DOI：10.1371/journal.pone.0324004.t003
• 发布日期：2025-05-19
• 文件数：1 个
• 文件总体积：5.63 KB
• 源站页面：https://plos.figshare.com/articles/dataset/Performance_of_models_predicting_/29102501

命令行下载

curl -L -o "Table 3.xls" "https://ndownloader.figshare.com/files/54654017"

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

Objective
The prevalence of arteriovenous fistula (AVF) dysfunction and its associated complications among patients undergoing maintenance hemodialysis (MHD) underscores the necessity for predictive models aimed at early diagnosis and intervention. Although various models for predicting AVF risk have emerged, a comprehensive review of their advancements and challenges is currently lacking. This study aims to systematically evaluate risk prediction models for AVF failure in MHD patients, thereby providing insights for clinical practitioners in selecting or developing more effective risk assessment tools.
Methods
A systematic search was conducted across multiple databases, including PubMed, Embase, Web of Science, Cochrane Library, ClinicalTrials.gov registry platform,the China Biomedical Literature Database, CNKI, WanFang Database, and the VIP Database, focusing on studies related to risk prediction models for AVF failure in MHD patients. Google Scholar was also searched to retrieve gray literature. The search encompassed literature from the inception of these databases up to August 1, 2024. Two independent researchers performed literature screening and data extraction, utilizing the Prediction Model Risk of Bias Assessment Tool to evaluate the methodological quality of the included studies.
Results
Initially, 2,052 studies were identified. After thorough screening, 11 studies were ultimately included, detailing 11 distinct risk prediction models for AVF failure in MHD patients. The sample sizes of these studies ranged from 126 to 14,892 participants. The identified models exhibited varying predictive performances, highlighting common limitations such as small sample sizes, improper handling of missing data, and a lack of external validation.
Conclusion
The risk prediction models for AVF failure among MHD patients demonstrate adequate predictive performance; however, the overall quality of the research necessitates improvement. Future studies should prioritize refining research design and reporting processes, as well as validating and enhancing existing models to ascertain their effectiveness and feasibility in clinical practice.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0324004.t003（source: https://plos.figshare.com/articles/dataset/Performance_of_models_predicting_/29102501）。