概率单位回归结果
基于英国ICU脓毒症患者数据,分析学习效应与规模经济对死亡率影响的概率单位回归结果数据集。

基本信息
资源简介
概率单位回归结果
【数据集背景】
本数据集源站标题为《Probit regression results.》。源站首次发布:2025-03-26。
【数据内容】
源站原始描述:Objective To evaluate the underlying mechanism of the volume-outcome relationship, namely learning-by-doing and scale economies in patients with sepsis. Design and study setting Retrospective cohort study of adult patients with sepsis between 1 January 2010 and 31 December 2016 in 231 intensive care units (ICUs) in the UK. Participants The patient was the primary unit of analysis. Patient and ICU characteristics were included for risk adjustment. Demographic and clinical data were extracted from…
【数据结构与技术规格】
文件数 1 个,合计 13.82 KB。Table 2.xls:application/vnd.ms-excel,13.82 KB。数据集 DOI:10.1371/journal.pone.0318808.t002。
【主题与分类】
源站学科分类:Medicine、Cell Biology、Biotechnology、Developmental Biology、Marine Biology、Cancer。主题标签:31 december 2016、1 january 2010、study provides evidence、acute hospital mortality、scale </ p、quarterly sepsis volume、hospital mortality、study finds、xlink ">、stronger association。
【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/Probit_regression_results_/28671047
使用许可:CC BY 4.0(https://creativecommons.org/licenses/by/4.0/)。
规范引用:Maharaj, Ritesh (2025). Probit regression results.. 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:Probit regression results.
- DOI:10.1371/journal.pone.0318808.t002
- 发布日期:2025-03-26
- 文件数:1 个
- 文件总体积:13.82 KB
- 源站页面:https://plos.figshare.com/articles/dataset/Probit_regression_results_/28671047
命令行下载
curl -L -o "Table 2.xls" "https://ndownloader.figshare.com/files/53249765"
文件清单
Table 2.xls(13.82 KB)
文件总体积 13.82 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
To evaluate the underlying mechanism of the volume-outcome relationship, namely learning-by-doing and scale economies in patients with sepsis.
Design and study setting
Retrospective cohort study of adult patients with sepsis between 1 January 2010 and 31 December 2016 in 231 intensive care units (ICUs) in the UK.
Participants
The patient was the primary unit of analysis. Patient and ICU characteristics were included for risk adjustment. Demographic and clinical data were extracted from the Intensive Care National Audit and Research Centre (ICNARC) Case Mix Programme database.
Study design
We used the lags of quarterly sepsis volume in the ICU as a measure of the learning-by-doing effect.
Outcome measure
The outcome of hospital mortality after ICU admission for sepsis was assessed using a multilevel probit regression model of patients nested in ICUs over quarters.
Data collection/extraction methods
Critically ill patients with sepsis were identified by the Sepsis-3 consensus criteria.
Results
Our study identified a cohort of 273001 patients with sepsis admitted to 231 ICUs in the UK. Our study finds that in comparison with contemporaneous volume, lagged volume had a stronger association with acute hospital mortality. This implies that the dynamic learning-by-doing effect is more important than the static economies of scale effect. This finding was consistent across alternate specifications of learning-by-doing.
Conclusions
The study provides evidence that the underlying mechanism for the volume-outcome relationship is learning-by-doing and not the static economies of scale. ICUs caring for patients with sepsis tend to improve by experience.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0318808.t002(source: https://plos.figshare.com/articles/dataset/Probit_regression_results_/28671047)。
数据缺失?
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