脊柱手术后手术部位感染风险评分系统数据集
用于预测脊柱手术后手术部位感染风险的评分系统数据集,包含2347例患者的术前、术中及术后表格数据。

基本信息
资源简介
脊柱手术后手术部位感染风险评分系统数据集
【一句话概览】
本数据集是 figshare 公开的结构化表格数据集,收录 8 个文件、合计 4.03 MB,主题涵盖 Surgical Wound Infection、Spine、Risk Factors、Risk Assessment,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《A SCORING SYSTEM TO PREDICT THE RISK OF SURGICAL SITE INFECTIONS AFTER SPINAL SURGERY》。配套论文《A SCORING SYSTEM TO PREDICT THE RISK OF SURGICAL SITE INFECTIONS AFTER SPINAL SURGERY》发表于 Coluna/Columna(2021),DOI:10.1590/s1808-185120212003251045。源站首次发布:2022-05-27。
【数据内容】
源站原始描述:ABSTRACT Objective This retrospective clinical study was carried out to generate and cross-validate a scoring system for the identification of patients at risk of SSIs after spinal surgery. Methods A retrospective study was conducted, which included patients who underwent spinal surgery. The potential variables for SSIs were extracted from the database, including preoperative, intraoperative and postoperative risk factors for univariate and multivariate regression analyses. Results A total of 23…
中文译文(机器翻译,仅供快速理解,请以原文为准):摘要 目的 本回顾性临床研究旨在生成并交叉验证一种用于识别脊柱手术后SSIs风险患者的评分系统。 方法 开展了一项回顾性研究,纳入接受脊柱手术的患者。从数据库中提取SSIs的潜在变量,包括术前、术中和术后危险因素,并进行单因素和多因素回归分析。 结果 本回顾性临床研究共纳入2347例患者。53例患者(2.2%)发生术后SSIs。多因素logistic回归分析显示,脊柱手术后SSIs的危险因素如下:糖尿病(P=0.029)、体重指数(P=0.008)、低血清钙浓度(P=0.012)、术前和术后白蛋白低(P=0.023,P=0.037)、手术节段超过3个(P=0.008)、手术时间超过180分钟(P=0.019)、估计失血量(P=0.011)、术后血红蛋白低(P=0.017)和引流时间延长(P=0.025)。上述每个因素均为风险评分贡献1分。验证集中低危、中危、高危和极高危类别的预测发生率分别为1.4%、12%、41.6%和66.6%。 结论 我们的评分系统可简便且经过验证地对脊柱手术后SSIs进行风险分层。证据等级III;横断面观察性研究。
【数据结构与技术规格】
文件数 8 个,合计 4.03 MB。数据集 DOI:10.6084/m9.figshare.19906441.v1。
【主题与分类】
源站学科分类:Orthopaedics、Rehabilitation and Therapy (excl. Physiotherapy)。主题标签:Surgical Wound Infection、Spine、Risk Factors、Risk Assessment。
【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://scielo.figshare.com/articles/dataset/A_SCORING_SYSTEM_TO_PREDICT_THE_RISK_OF_SURGICAL_SITE_INFECTIONS_AFTER_SPINAL_SURGERY/19906441
使用许可:CC BY 4.0(https://creativecommons.org/licenses/by/4.0/)。
规范引用:STEPANOV, IVAN ANDREEVICH; BELOBORODOV, VLADIMIR ANATOL’EVICH; SHAMEEVA, MARIYA ANATOL’EVNA; BORISOV, EDUARD BORISOVICH (2021). A SCORING SYSTEM TO PREDICT THE RISK OF SURGICAL SITE INFECTIONS AFTER SPINAL SURGERY. SciELO journals. Dataset
【使用注意】
· 源站最后修改于 2023-06-01,引用时请注意版本时效。
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:A SCORING SYSTEM TO PREDICT THE RISK OF SURGICAL SITE INFECTIONS AFTER SPINAL SURGERY
- DOI:10.6084/m9.figshare.19906441.v1
- 发布日期:2022-05-27
- 文件数:8 个
- 文件总体积:4.03 MB
- 源站页面:https://scielo.figshare.com/articles/dataset/A_SCORING_SYSTEM_TO_PREDICT_THE_RISK_OF_SURGICAL_SITE_INFECTIONS_AFTER_SPINAL_SURGERY/19906441
命令行下载
curl -L -o "03f208d983410b9458be481acb2e02b401c956eb.tif" "https://ndownloader.figshare.com/files/35354791"
curl -L -o "1b643c18a9044bf6c9027f6e0fa1e2aa58a7333a.png" "https://ndownloader.figshare.com/files/35354794"
curl -L -o "72e609ea980ba7f793e6e3744aefcb31edf80c99.jpg" "https://ndownloader.figshare.com/files/35354797"
curl -L -o "Table_1.xls" "https://ndownloader.figshare.com/files/35354800"
curl -L -o "Table_2.xls" "https://ndownloader.figshare.com/files/35354803"
curl -L -o "Table_3.xls" "https://ndownloader.figshare.com/files/35354806"
curl -L -o "Table_4.xls" "https://ndownloader.figshare.com/files/35354809"
curl -L -o "Table_5.xls" "https://ndownloader.figshare.com/files/35354812"
文件清单
03f208d983410b9458be481acb2e02b401c956eb.tif(3.88 MB)1b643c18a9044bf6c9027f6e0fa1e2aa58a7333a.png(110.31 KB)72e609ea980ba7f793e6e3744aefcb31edf80c99.jpg(4.27 KB)Table_1.xls(5.63 KB)Table_2.xls(5.63 KB)Table_3.xls(5.63 KB)Table_4.xls(5.63 KB)Table_5.xls(5.63 KB)
文件总体积 4.03 MB(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
ABSTRACT Objective This retrospective clinical study was carried out to generate and cross-validate a scoring system for the identification of patients at risk of SSIs after spinal surgery. Methods A retrospective study was conducted, which included patients who underwent spinal surgery. The potential variables for SSIs were extracted from the database, including preoperative, intraoperative and postoperative risk factors for univariate and multivariate regression analyses. Results A total of 2347 patients were included in this retrospective clinical study. Postoperative SSIs were observed in 53 patients (2.2%). The multivariate logistic regression analysis revealed the following risk factors for SSIs after spinal surgery: diabetes mellitus ( P =0.029), body mass index ( P =0.008), low serum calcium concentration ( P =0.012), low pre- and postoperative albumin ( P =0.023, P =0.037), more than three operated segments ( P =0.008), operation time of more than 180 minutes ( P =0.019), estimated blood loss ( P =0.011), low postoperative hemoglobin ( P =0.017) and prolonged drainage time ( P =0.025). Each of these factors contributed 1 point to the risk score. The predicted rates of incidence for the low-, intermediate-, high-, and extremely high-risk categories in the validation set were 1.4%, 12%, 41.6%, and 66.6%, respectively. Conclusions Our scoring system allows for easy and validated risk stratification of SSIs after spinal surgery. Level of evidence III; Cross-sectional Observational Study.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.6084/m9.figshare.19906441.v1(source: https://scielo.figshare.com/articles/dataset/A_SCORING_SYSTEM_TO_PREDICT_THE_RISK_OF_SURGICAL_SITE_INFECTIONS_AFTER_SPINAL_SURGERY/19906441)。
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。
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