模型预测因素分类

数据集获取（Figshare）

• 源站标题：Classification of model predictive factors.
• DOI：10.1371/journal.pone.0333466.t005
• 发布日期：2025-10-06
• 文件数：1 个
• 文件总体积：5.63 KB
• 源站页面：https://plos.figshare.com/articles/dataset/Classification_of_model_predictive_factors_/30288188

命令行下载

curl -L -o "Table 5.xls" "https://ndownloader.figshare.com/files/58521146"

文件清单

• Table 5.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
Peripherally inserted central venous catheters (PICC) associated bloodstream infections is a type of central line-associated bloodstream infection (CLABSI), the purpose of this scoping review was to analyse and summarize the risk prediction models for PICC-CLABSI to provide insights for clinical nursing practice.
Methods
A scoping review was conducted from six bibliographic databases including CNKI, Wanfang Database, VIP Chinese Journal Database, PubMed, Embase, and Web of Science, from inception to November 5, 2024. Screening was performed and relevant data was extracted independently by two researchers, the risk of bias was assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST).
Results
Eight studies met the inclusion criteria, which included two score models, six nomograms, and one ELM model. The included studies exhibited a high risk of bias, mainly due to methodological heterogeneity. Four models underwent external validation, two were assessed for goodness of fit. The most frequently identified predictors including catheter indwelling time, maintenance cycle/frequency, multilumen catheters, PICC parenteral nutrition, diabetes, and malignant tumors.
Conclusion
The risk prediction models for PICC-CLABSI demonstrated strong predictive performance. Future research should carefully address all elements of PROBAST framework during study design phase. This will facilitate both internal and external validation, as well as differentiation, calibration, and evaluation of clinical practicality. The ultimate objective is to develop a PICC-CLABSI risk prediction model that exhibits low bias risk, robust predictive performance, and high clinical applicability.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0333466.t005（source: https://plos.figshare.com/articles/dataset/Classification_of_model_predictive_factors_/30288188）。