手术部位感染风险研究数据集
包含手术部位感染患者数据的SPSS格式数据集，用于分析缝合类型与感染风险的关系。
基本信息
资源简介
手术部位感染风险研究数据集
【一句话概览】
本数据集是 figshare 公开的结构化表格数据集，收录 1 个文件、合计 21.42 KB，主题涵盖 Surgical site infections、Triclosan coated suture、Obstetrics and Gynaecology，可用于统计建模、特征工程与队列分析，CC BY 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《Data Set.sav》。源站首次发布：2022-10-13。
【数据内容】
源站原始描述：The study was approved by the local ethic committee of Ben Arous hospital, Tunisia. Written consent for participation was obtained from each patient. Data collection was conducted in compliance with Tunisian laws regarding personal data protection. Statistical analysis was performed according to the intention-to-treat principle. Differences in the baseline characteristics and the primary and secondary outcome measures between the groups were compared with the chi-squared test for nominal variabl…
中文译文（机器翻译，仅供快速理解，请以原文为准）：该研究获得了突尼斯Ben Arous医院当地伦理委员会的批准。每位患者均签署了书面知情同意书。数据收集过程严格遵守突尼斯关于个人数据保护的法律。 统计分析按照意向性治疗原则进行。组间基线特征及主要和次要结局指标的差异比较采用以下方法：名义变量采用卡方检验，正态分布的连续变量采用独立样本Student''s t检验，偏态分布的连续变量采用非参数Mann-Whitney U检验。 为探讨除缝线类型外与SSI发生相关的其他危险因素，首先对所有自变量进行双变量分析。任何具有统计学显著意义或接近显著意义（p <0.10）的变量均被视为潜在混杂因素。采用多因素logistic回归模型，纳入双变量分析中识别的所有可能混杂因素的影响，以评估缝线类型对SSI发生的校正效应。为比较伤口愈合障碍，进行了单因素分析。以p值 < 0.05（双侧）为差异有统计学意义。
【数据结构与技术规格】
文件数 1 个，合计 21.42 KB。Data Set.sav：application/octet-stream，21.42 KB。数据集 DOI：10.6084/m9.figshare.21330624.v4。
【主题与分类】
源站学科分类：Obstetrics and gynaecology。主题标签：Surgical site infections、Triclosan coated suture、Obstetrics and Gynaecology。
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://figshare.com/articles/dataset/Data_Set_sav/21330624
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Bettaieb, Hajer; Mbarki, wael (2022). Data Set.sav. figshare. 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：Data Set.sav
- DOI：10.6084/m9.figshare.21330624.v4
- 发布日期：2022-10-13
- 文件数：1 个
- 文件总体积：21.42 KB
- 源站页面：https://figshare.com/articles/dataset/Data_Set_sav/21330624
命令行下载
curl -L -o "Data Set.sav" "https://ndownloader.figshare.com/files/37856580"
文件清单
Data Set.sav（21.42 KB）
文件总体积 21.42 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
The study was approved by the local ethic committee of Ben Arous hospital, Tunisia. Written consent for participation was obtained from each patient. Data collection was conducted in compliance with Tunisian laws regarding personal data protection.
Statistical analysis was performed according to the intention-to-treat principle. Differences in the baseline characteristics and the primary and secondary outcome measures between the groups were compared with the chi-squared test for nominal variables, the independent Student’s t-test for continuous variables with normal distributions, and the nonparametric Mann-Whitney U test for continuous variables with skewed distributions.
To address risk factors involved in SSI development other than suture type, a bivariate analysis was first conducted on all independent variables. Any variable with a statistically significant response, or very close to it (p <0.10), was considered a potential confounding factor. A multivariate logistic regression model, which considered the impact of all possible confounding factors identified in the bivariate analysis, was used to measure the adjusted effect of suture type on the development of SSI. To compare wound healing disturbances an univariate analysis was conducted. A p-value < 0.05 (two-sided) was considered to be significant.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.6084/m9.figshare.21330624.v4（source: https://figshare.com/articles/dataset/Data_Set_sav/21330624）。
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。
⚡️ 需要数据支持或标注服务？立即联系我们获取专业方案。
