患者非医疗特征对肿瘤多学科团队集体医疗决策的影响数据集

数据集获取（Figshare）

• 源站标题：Patients’ Non-Medical Characteristics Contribute to Collective Medical Decision-Making at Multidisciplinary Oncological Team Meetings
• DOI：10.1371/journal.pone.0154969
• 发布日期：2016-05-11
• 文件数：2 个
• 文件总体积：19.86 KB
• 源站页面：https://plos.figshare.com/articles/dataset/Patients_Non-Medical_Characteristics_Contribute_to_Collective_Medical_Decision-Making_at_Multidisciplinary_Oncological_Team_Meetings/3378787

命令行下载

curl -L -o "S1 File.SAV" "https://ndownloader.figshare.com/files/5223769"
curl -L -o "S2 File.SAV" "https://ndownloader.figshare.com/files/5223772"

文件清单

• S1 File.SAV（4.44 KB）
• S2 File.SAV（15.42 KB）

文件总体积 19.86 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
The contribution of patients’ non-medical characteristics to individual physicians’ decision-making has attracted considerable attention, but little information is available on this topic in the context of collective decision-making. Medical decision-making at cancer centres is currently carried out using a collective approach, at MultiDisciplinary Team (MDT) meetings. The aim of this study was to determine how patients’ non-medical characteristics are presented at MDT meetings and how this information may affect the team’s final medical decisions.
Design
Observations were conducted at a French Cancer Centre during MDT meetings at which non-standard cases involving some uncertainty were discussed from March to May 2014. Physicians’ verbal statements and predefined contextual parameters were collected with a non-participant observational approach. Non numerical data collected in the form of open notes were then coded for quantitative analysis. Univariate and multivariate statistical analyses were performed.
Results
In the final sample of patients’ records included and discussed (N = 290), non-medical characteristics were mentioned in 32.8% (n = 95) of the cases. These characteristics corresponded to demographics in 22.8% (n = 66) of the cases, psychological data in 11.7% (n = 34), and relational data in 6.2% (n = 18). The patient’s age and his/her “likeability” were the most frequently mentioned characteristics. In 17.9% of the cases discussed, the final decision was deferred: this outcome was positively associated with the patients’ non-medical characteristics and with uncertainty about the outcome of the therapeutic options available.
Limitations
The design of the study made it difficult to draw definite cause-and-effect conclusions.
Conclusion
The Social Representations approach suggests that patients’ non-medical characteristics constitute a kind of tacit professional knowledge that may be frequently mobilised in physicians’ everyday professional practice. The links observed between patients’ attributes and the medical decisions made at these meetings show that these attributes should be taken into account in order to understand how medical decisio

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0154969（source: https://plos.figshare.com/articles/dataset/Patients_Non-Medical_Characteristics_Contribute_to_Collective_Medical_Decision-Making_at_Multidisciplinary_Oncological_Team_Meetings/3378787）。