Building a Systematic Online Living Evidence Summary of COVID-19 Research

Abstract

Throughout the global coronavirus pandemic, we have seen an unprecedented volume of COVID-19 researchpublications. 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.

Systematic online living evidence summaries: emerging tools to accelerate evidence synthesis

“…Further, by implementing a crowd-sourced approach to build up sets of annotated data at a quicker pace, we could train tailored automation approaches. Using this approach, we created a pilot COVID-SOLES, a resource which was updated on a weekly basis with new evidence in the emerging pandemic [ 57 ]. More recently we created the SPRINT-SOLES, in collaboration with over 20 experts in plant protection products who annotated subsets of the literature to use as training data for automated tools [ 58 ].…”

Systematic Online Living Evidence Summaries: emerging tools to accelerate evidence synthesis

“…Using this approach, we created a pilot COVID-SOLES, a resource which was updated on a weekly basis with new evidence in the emerging pandemic (Hair, Sena, et al, 2021). More recently we created the SPRINT-SOLES, in collaboration with over 20 experts in plant protection products who annotated subsets of the literature to use as training data for automated tools (Hair, 2022b).…”