人群特征(N=187)
包含187例局限性乳腺癌患者治疗延迟数据的表格数据集,用于分析COVID-19大流行对首次治疗时间的影响。

基本信息
资源简介
人群特征(N=187)
【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 9.73 KB,主题涵盖 log link function、32 – 54、localized breast cancer、187 patients included、first treatment regardless,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《Population characteristics (N = 187).》。源站首次发布:2024-05-31。
【数据内容】
源站原始描述:Background Longer times between diagnosis and treatments of cancer patients have been estimated as effects of the COVID-19 pandemic. However, relatively few studies attempted to estimate actual delay to treatment at the patient level. Objective To assess changes in delays to first treatment and surgery among newly diagnosed patients with localized breast cancer (BC) during the COVID-19 pandemic. Methods We used data from the PAPESCO-19 multicenter cohort study, which included patients from 4 Fre…
【数据结构与技术规格】
文件数 1 个,合计 9.73 KB。Table 1.xls:application/vnd.ms-excel,9.73 KB。数据集 DOI:10.1371/journal.pone.0304556.t001。
【主题与分类】
源站学科分类:Medicine、Cell Biology、Molecular Biology、Biotechnology、Developmental Biology、Cancer。主题标签:log link function、32 – 54、localized breast cancer、187 patients included、first treatment regardless、treatment among patients、estimate actual delay、19 pandemic exposure、included patients、cancer patients。
【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://plos.figshare.com/articles/dataset/Population_characteristics_N_187_/25945734
使用许可:CC BY 4.0(https://creativecommons.org/licenses/by/4.0/)。
规范引用:Zhou, Ke; Robert, Marie; Seegers, Valérie; Blanc-Lapierre, Audrey; Savouroux, Stéphane; Bigot, Frédéric; et al. (2024). Population characteristics (N = 187).. PLOS ONE. 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:Population characteristics (N = 187).
- DOI:10.1371/journal.pone.0304556.t001
- 发布日期:2024-05-31
- 文件数:1 个
- 文件总体积:9.73 KB
- 源站页面:https://plos.figshare.com/articles/dataset/Population_characteristics_N_187_/25945734
命令行下载
curl -L -o "Table 1.xls" "https://ndownloader.figshare.com/files/46752126"
文件清单
Table 1.xls(9.73 KB)
文件总体积 9.73 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
Longer times between diagnosis and treatments of cancer patients have been estimated as effects of the COVID-19 pandemic. However, relatively few studies attempted to estimate actual delay to treatment at the patient level.
Objective
To assess changes in delays to first treatment and surgery among newly diagnosed patients with localized breast cancer (BC) during the COVID-19 pandemic.
Methods
We used data from the PAPESCO-19 multicenter cohort study, which included patients from 4 French comprehensive cancer centers. We measured the delay to first treatment as the number of days between diagnosis and the first treatment regardless of whether this was neoadjuvant chemotherapy or surgery. COVID-19 pandemic exposure was estimated with a composite index that considered both the severity of the pandemic and the level of lockdown restrictions. We ran generalized linear models with a log link function and a gamma distribution to model the association between delay and the pandemic.
Results
Of the 187 patients included in the analysis, the median delay to first treatment was 42 (IQR:32–54) days for patients diagnosed before and after the start of the 1st lockdown (N = 99 and 88, respectively). After adjusting for age and centers of inclusion, a higher composite pandemic index (> = 50 V.S. Conclusion
We found evidence of no direct impact of the pandemic on the actual delay to treatment among patients with localized BC.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0304556.t001(source: https://plos.figshare.com/articles/dataset/Population_characteristics_N_187_/25945734)。
需要用于商业化训练或医疗器械注册(NMPA/FDA)?
公开数据往往缺乏完整的病理回证与专家标注。千方医数集依托陕川双基地与超十家三甲医院直连,提供完全合规、去标识化、支持注册级质控的专病数据集交易服务。




