Commentary on the commentary "On measurement error, PSA doubling time, and prostate cancer" [0.03%]
对“论测量误差，PSA倍增时间和前列腺癌”的评论的评论
Lawrence L Kupper,Sandra L Martin
Lawrence L Kupper
All are not created equal: Method descriptions in an epidemiology publication differ among media summaries - A case study comparison [0.03%]
并非所有方法描述都同等重要：流行病学出版物中的媒体总结存在差异 - 一个案例研究比较
Lilianne Samad,J E Reed
Lilianne Samad
It is common to see mass media headlines about health-related topics in traditional and online news outlets, as well as on social media platforms. What a consumer might not realize is that often these headlines are a distillation of results...
Modeling the determinants of attrition in a two-stage epilepsy prevalence survey in Nairobi using machine learning [0.03%]
利用机器学习模型分析内罗毕癫痫流行病学调查中脱落的因素
Daniel M Mwanga,Isaac C Kipchirchir,George O Muhua et al.
Daniel M Mwanga et al.
Background: Attrition is a challenge in parameter estimation in both longitudinal and multi-stage cross-sectional studies. Here, we examine utility of machine learning to predict attrition and identify associated factors ...
Multisystem inflammatory syndrome in children (MIS-C) associated with COVID-19, clinical characteristics: A multi-center observational study from Jordan [0.03%]
与COVID-19相关的儿童多系统炎症综合征（MIS-C）的临床特征：一项来自约旦的多中心观察性研究
Marwan Shalabi,Salam Ghanem,Iyad Al-Ammouri et al.
Marwan Shalabi et al.
Objective: Multisystem inflammatory syndrome of childhood (MIS-C) is a newly recognized entity associated with COVID-19 in children. The objective was to describe the clinical course for 74 patients diagnosed with this di...
Nurse-led medication self-management intervention in the improvement of medication adherence in adult patients with multi-morbidity: A Protocol for a Feasibility Randomized controlled trial [0.03%]
多重疾病成人患者药物自我管理能力的护士引导干预对其用药依从性改善的可行性随机对照试验方案研究
Kalpana Singh,George V Joy,Asma Al Bulushi et al.
Kalpana Singh et al.
Background: Multimorbidity in adult patients puts them at a considerable risk of not taking their medications as prescribed. It is well known that patients with chronic conditions with self-management help is an excellent...
AI-assisted exposure-response data analysis: Quantifying heterogeneous causal effects of exposures on survival times [0.03%]
基于人工智能的暴露反应数据分析：量化暴露对生存时间异质性因果效应
Louis Anthony Cox Jr,R Jeffrey Lewis,Saumitra V Rege et al.
Louis Anthony Cox Jr et al.
AI-assisted data analysis can help risk analysts better understand exposure-response relationships by making it relatively easy to apply advanced statistical and machine learning methods, check their assumptions, and interpret their results...
ACCREDIT: Validation of clinical score for progression of COVID-19 while hospitalized [0.03%]
ACCREDIT：评估临床预测评分在预测住院新冠患者疾病进展中的有效性
Vinicius Lins Costa Ok Melo,Pedro Emmanuel Alvarenga Americano do Brasil
Vinicius Lins Costa Ok Melo
COVID-19 is no longer a global health emergency, but it remains challenging to predict its prognosis. Objective: To develop and validate an instrument to ...
Interaction between opium use and cigarette smoking on bladder cancer: An inverse probability weighting approach based on a multicenter case-control study in Iran [0.03%]
基于伊朗多中心病例对照研究的逆概率加权法分析大麻素使用与吸烟对膀胱癌的交互作用
Rahim Akrami,Maryam Hadji,Hamideh Rashidian et al.
Rahim Akrami et al.
Introduction: Opium and cigarette smoking have been identified as significant cancer risk factors. Recently, the International Agency for Research on Cancer (IARC) classified opium as a Group 1 carcinogen in 2020. ...
Lower limb lymphoedema-related mental depression: A systematic review and meta-analysis of non-cancer-related studies [0.03%]
下肢淋巴水肿相关性抑郁的系统评价和meta分析（非癌症相关）
Tegene Atamenta Kitaw,Addisu Getie,Solomon Gebremichael Asgedom et al.
Tegene Atamenta Kitaw et al.
Background: Lower limb lymphoedema, characterized by persistent swelling in the legs due to lymphatic dysfunction, not only imposes a physical burden but is also associated with significant mental depression. While emergi...
Pieter Streicher,Alex Broadbent,Joel Hellewell
Pieter Streicher
During the Covid-19 pandemic, the best-performing modelling groups were not always the best-resourced. This paper seeks to understand and learn from notable predictions in two reports by the UK's Scientific Advisory Group for Emergencies (S...