Model performance.
医院诱发性谵妄预后模型的系统综述性能数据集，含模型开发与验证的偏倚评估指标。
基本信息
资源简介
Model performance.
【数据集背景】
源站首次发布：2023-08-17。
【数据内容】
源站原始描述：Purpose The purpose of this systematic review was to assess risk of bias in existing prognostic models of hospital-induced delirium for medical-surgical units. Methods APA PsycInfo, CINAHL, MEDLINE, and Web of Science Core Collection were searched on July 8, 2022, to identify original studies which developed and validated prognostic models of hospital-induced delirium for adult patients who were hospitalized in medical-surgical units. The Checklist for Critical Appraisal and Data Extraction for…
【数据结构与技术规格】
文件数 1 个，合计 17.92 KB。Table 2.xls：undefined，17.92 KB。数据集 DOI：10.1371/journal.pone.0285527.t002。
【主题与分类】
源站学科分类：Medicine、Cell Biology、Biotechnology、Biological Sciences not elsewhere classified、Mathematical Sciences not elsewhere classified、Developmental Biology。主题标签：tailor preventive interventions、science core collection、ongoing scientific challenge、valid prognostic model、three model validation、validate risk factors、prediction model risk、prediction modelling studies、validated prognostic models、existing prognostic models。
【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/Model_performance_/23980333
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Snigurska, Urszula A.; Liu, Yiyang; Ser, Sarah E.; Macieira, Tamara G. R.; Ansell, Margaret; Lindberg, David; et al. (2023). Model performance.. PLOS ONE. Dataset
下载信息
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使用方式
数据集获取（Figshare）
- 源站标题：Model performance.
- DOI：10.1371/journal.pone.0285527.t002
- 发布日期：2023-08-17
- 文件数：1 个
- 文件总体积：17.92 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Model_performance_/23980333
命令行下载
curl -L -o "Table 2.xls" "https://ndownloader.figshare.com/files/42044689"
文件清单
Table 2.xls（17.92 KB）
文件总体积 17.92 KB（源站 API 实测）。
数据说明
Purpose
The purpose of this systematic review was to assess risk of bias in existing prognostic models of hospital-induced delirium for medical-surgical units.
Methods
APA PsycInfo, CINAHL, MEDLINE, and Web of Science Core Collection were searched on July 8, 2022, to identify original studies which developed and validated prognostic models of hospital-induced delirium for adult patients who were hospitalized in medical-surgical units. The Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies was used for data extraction. The Prediction Model Risk of Bias Assessment Tool was used to assess risk of bias. Risk of bias was assessed across four domains: participants, predictors, outcome, and analysis.
Results
Thirteen studies were included in the qualitative synthesis, including ten model development and validation studies and three model validation only studies. The methods in all of the studies were rated to be at high overall risk of bias. The methods of statistical analysis were the greatest source of bias. External validity of models in the included studies was tested at low levels of transportability.
Conclusions
Our findings highlight the ongoing scientific challenge of developing a valid prognostic model of hospital-induced delirium for medical-surgical units to tailor preventive interventions to patients who are at high risk of this iatrogenic condition. With limited knowledge about generalizable prognosis of hospital-induced delirium in medical-surgical units, existing prognostic models should be used with caution when creating clinical practice policies. Future research protocols must include robust study designs which take into account the perspectives of clinicians to identify and validate risk factors of hospital-induced delirium for accurate and generalizable prognosis in medical-surgical units.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0285527.t002（source: https://plos.figshare.com/articles/dataset/Model_performance_/23980333）。
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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