髋部骨折后30天死亡率管理数据验证数据集

数据集获取（Figshare）

• 源站标题：Variation between Hospitals with Regard to Diagnostic Practice, Coding Accuracy, and Case-Mix. A Retrospective Validation Study of Administrative Data versus Medical Records for Estimating 30-Day Mortality after Hip Fracture
• DOI：10.1371/journal.pone.0156075
• 发布日期：2016-05-20
• 文件数：5 个
• 文件总体积：1.85 MB
• 源站页面：https://plos.figshare.com/articles/dataset/Variation_between_Hospitals_with_Regard_to_Diagnostic_Practice_Coding_Accuracy_and_Case-Mix_A_Retrospective_Validation_Study_of_Administrative_Data_versus_Medical_Records_for_Estimating_30-Day_Mortality_after_Hip_Fracture/3396601

命令行下载

curl -L -o "S1 Fig.TIF" "https://ndownloader.figshare.com/files/5306371"
curl -L -o "S1 Text.PDF" "https://ndownloader.figshare.com/files/5306386"
curl -L -o "S2 Text.PDF" "https://ndownloader.figshare.com/files/5306389"
curl -L -o "S3 Text.PDF" "https://ndownloader.figshare.com/files/5306392"
curl -L -o "S4 Text.PDF" "https://ndownloader.figshare.com/files/5306407"

文件清单

• S1 Fig.TIF（573.10 KB）
• S1 Text.PDF（358.39 KB）
• S2 Text.PDF（340.93 KB）
• S3 Text.PDF（317.85 KB）
• S4 Text.PDF（264.39 KB）

文件总体积 1.85 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

Background
The purpose of this study was to assess the validity of patient administrative data (PAS) for calculating 30-day mortality after hip fracture as a quality indicator, by a retrospective study of medical records.
Methods
We used PAS data from all Norwegian hospitals (2005–2009), merged with vital status from the National Registry, to calculate 30-day case-mix adjusted mortality for each hospital (n = 51). We used stratified sampling to establish a representative sample of both hospitals and cases. The hospitals were stratified according to high, low and medium mortality of which 4, 3, and 5 hospitals were sampled, respectively. Within hospitals, cases were sampled stratified according to year of admission, age, length of stay, and vital 30-day status (alive/dead). The final study sample included 1043 cases from 11 hospitals. Clinical information was abstracted from the medical records. Diagnostic and clinical information from the medical records and PAS were used to define definite and probable hip fracture. We used logistic regression analysis in order to estimate systematic between-hospital variation in unmeasured confounding. Finally, to study the consequences of unmeasured confounding for identifying mortality outlier hospitals, a sensitivity analysis was performed.
Results
The estimated overall positive predictive value was 95.9% for definite and 99.7% for definite or probable hip fracture, with no statistically significant differences between hospitals. The standard deviation of the additional, systematic hospital bias in mortality estimates was 0.044 on the logistic scale. The effect of unmeasured confounding on outlier detection was small to moderate, noticeable only for large hospital volumes.
Conclusions
This study showed that PAS data are adequate for identifying cases of hip fracture, and the effect of unmeasured case mix variation was small. In conclusion, PAS data are adequate for calculating 30-day mortality after hip-fracture as a quality indicator in Norway.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0156075（source: https://plos.figshare.com/articles/dataset/Variation_between_Hospitals_with_Regard_to_Diagnostic_Practice_Coding_Accuracy_and_Case-Mix_A_Retrospective_Validation_Study_of_Administrative_Data_versus_Medical_Records_for_Estimating_30-Day_Mortality_after_Hip_Fracture/3396601）。