A Systematic Online Living Evidence Summary of experimental Alzheimer’s disease research

严厉 透明度(行为) 数据科学 步伐 计算机科学 工作流程 转化研究 系统回顾 质量(理念) 循证医学 循证实践 医学 梅德林 病理 替代医学 政治学 哲学 几何学 数学 计算机安全 大地测量学 认识论 数据库 法学 地理
作者
Kaitlyn Hair,Olena Maksym,Olena Maksym,Robert M. Califf,Olena Maksym
出处
期刊:Journal of Neuroscience Methods [Elsevier BV]
卷期号:409: 110209-110209
标识
DOI:10.1016/j.jneumeth.2024.110209
摘要

Despite extensive investment, the development of effective treatments for Alzheimer's disease (AD) has been largely unsuccessful. To improve translation, it is crucial to ensure the quality and reproducibility of foundational evidence generated from laboratory models. Systematic reviews play a key role in providing an unbiased overview of the evidence, assessing rigour and reporting, and identifying factors that influence reproducibility. However, the sheer pace of evidence generation is prohibitive to evidence synthesis and assessment. To address these challenges, we have developed AD-SOLES, an integrated workflow of automated tools that collect, curate, and visualise the totality of evidence from in vivo experiments. AD-SOLES is a publicly accessible interactive dashboard aiming to surface and expose data from in vivo experiments. It summarises the latest evidence, tracks reporting quality and transparency, and allows research users to easily locate evidence relevant to their specific research question. Using automated screening methodologies within AD-SOLES, systematic reviews can begin at an accelerated starting point compared to traditional approaches. Furthermore, through text-mining approaches within the full-text of publications, users can identify research of interest using specific models, outcomes, or interventions without relying on details in the title and/or abstract. By automating the collection, curation, and visualisation of evidence from in vivo experiments, AD-SOLES addresses the challenges posed by the rapid pace of evidence generation. AD-SOLES aims to offer guidance for research improvement, reduce research waste, highlight knowledge gaps, and support informed decision making for researchers, funders, patients, and the public.

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