Centre for Clinical Brain Sciences, The University of Edinburgh
Ezgi Tanriver Ayder
Centre for Clinical Brain Sciences, University of Edinburgh *Statement of potential conflicts of interest: ETA is an employee and shareholder of AstraZeneca. AstraZeneca, however, has no commercial interest in the material presented within this manuscript.
(1) Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil (2) Ronin Institute (globally distributed, see http://ronininstitute.org/)
Pain Research Group, Department of Surgery and Cancer, Imperial College London, Chelsea and Westminster Hospital, 369 Fulham Road, London, SW10 9NH, UK
Translational Neuroscience PhD Programme, Centre for Discovery Brain Sciences and the UK Dementia Research Institute, The University of Edinburgh, 1 George Square, Edinburgh EH8 9JZ, UK
King's College London, Department of Neuroscience Education
Helen Fielding
The Royal (Dick) School of Veterinary Studies (R(D)SVS) and the Roslin Institute, Hospital for Small Animals, Easter Bush Veterinary Centre, Midlothian EH25 9RG, UK.
Throughout the global coronavirus pandemic, we have seen an unprecedented volume of COVID-19 research publications. This vast body of evidence continues to grow, making it difficult for research users to keep up with the pace of evolving research findings. To enable the synthesis of this evidence for timely use by researchers, policymakers, and other stakeholders, we developed an automated workflow to collect, categorise, and visualise the evidence from primary COVID-19 research studies. We trained a crowd of volunteer reviewers to annotate studies by relevance to COVID-19, study objectives, and methodological approaches. Using these human decisions, we are training machine learning classifiers and applying text-mining tools to continually categorise the findings and evaluate the quality of COVID-19 evidence.
Building a Systematic Online Living Evidence Summary of COVID-19 Research. J Eur Assoc Health Info Libr [Internet]. 2021 Jun. 24 [cited 2026 Aug. 13];17(2):21-6. Available from: https://ojs.eahil.eu/JEAHIL/article/view/465