A hybrid machine learning framework to improve prediction of all-cause rehospitalization among elderly patients in Hong Kong [0.03%]
用于改善香港老年患者全因再入院预测的混合机器学习框架
Jingjing Guan,Eman Leung,Kin-On Kwok et al.
Jingjing Guan et al.
Background: Accurately estimating elderly patients' rehospitalisation risk benefits clinical decisions and service planning. However, research in rehospitalisation and repeated hospitalisation yielded only models with mod...
Misspecification of confounder-exposure and confounder-outcome associations leads to bias in effect estimates [0.03%]
错误指定混杂因素与暴露和混杂因素与结局的联系会导致效应估计偏差
Noah A Schuster,Judith J M Rijnhart,Lisa C Bosman et al.
Noah A Schuster et al.
Background: Confounding is a common issue in epidemiological research. Commonly used confounder-adjustment methods include multivariable regression analysis and propensity score methods. Although it is common practice to ...
Impact of sampling and data collection methods on maternity survey response: a randomised controlled trial of paper and push-to-web surveys and a concurrent social media survey [0.03%]
采样和数据收集方法对产妇调查反应的影响：纸质和网络推送调查的随机对照试验及同时进行的社会媒体调查
Siân Harrison,Fiona Alderdice,Maria A Quigley
Siân Harrison
Background: Novel survey methods are needed to tackle declining response rates. The 2020 National Maternity Survey included a randomised controlled trial (RCT) and social media survey to compare different combinations of ...
Randomized Controlled Trial
BMC medical research methodology. 2023 Jan 12;23(1):10. DOI:10.1186/s12874-023-01833-8 2023
The reporting of prognostic prediction models for obstetric care was poor: a cross-sectional survey of 10-year publications [0.03%]
用于产科护理的预后预测模型的报告很差：十年出版物的横断面调查
Chunrong Liu,Yana Qi,Xinghui Liu et al.
Chunrong Liu et al.
Background: To investigate the reporting of prognostic prediction model studies in obstetric care through a cross-sectional survey design. Methods: ...
Ayesha Sajjad,Matthijs M Versteegh,Irene Santi et al.
Ayesha Sajjad et al.
Objectives: Country-specific value sets for the EQ-5D are available which reflect preferences for health states elicited from the general population. This allows the transformation of responses on EQ-5D to health state ut...
Power and sample size analysis for longitudinal mixed models of health in populations exposed to environmental contaminants: a tutorial [0.03%]
环境污染物暴露人群健康纵向混合模型的功率和样本量分析教程
Kylie K Harrall,Keith E Muller,Anne P Starling et al.
Kylie K Harrall et al.
Background: When evaluating the impact of environmental exposures on human health, study designs often include a series of repeated measurements. The goal is to determine whether populations have different trajectories of...
Machine learning for predicting neurodegenerative diseases in the general older population: a cohort study [0.03%]
在普通老年人群中预测神经退行性疾病机器学习的研究：队列研究
Gloria A Aguayo,Lu Zhang,Michel Vaillant et al.
Gloria A Aguayo et al.
Background: In the older general population, neurodegenerative diseases (NDs) are associated with increased disability, decreased physical and cognitive function. Detecting risk factors can help implement prevention measu...
Semiparametric modelling of diabetic retinopathy among people with type II diabetes mellitus [0.03%]
II型糖尿病患者糖尿病性视网膜病变的半参数模型分析
Bezalem Eshetu Yirdaw,Legesse Kassa Debusho
Bezalem Eshetu Yirdaw
Background: The proportion of patients with diabetic retinopathy (DR) has grown with increasing number of diabetes mellitus patients in the world. It is among the major causes of blindness worldwide. The main objective of...
Studies with statistically significant effect estimates are more frequently published compared to non-significant estimates in oral health journals [0.03%]
与口腔健康期刊中的非显著估计值相比，具有统计学显著效果估计值的研究更经常被发表
Jadbinder Seehra,Hadil Khraishi,Nikolaos Pandis
Jadbinder Seehra
Background: Studies reporting statistically significant effect estimates tend to be more frequently published compared to studies reporting non-significant or equivalent estimates. Consequently, this may lead to distortio...
Predicting the risk of a clinical event using longitudinal data: the generalized landmark analysis [0.03%]
使用纵向数据预测临床事件的风险：广义地标分析
Yi Yao,Liang Li,Brad Astor et al.
Yi Yao et al.
Background: In the development of prediction models for a clinical event, it is common to use the static prediction modeling (SPM), a regression model that relates baseline predictors to the time to event. In many situati...