When screening titles and abstracts through Covidence, you have flexibility over how you sort the list of studies.
The following options are available:
- Most relevant - default
- Most recent
The sorting method can be updated via the sort option at the top of the screening list:
When sorting by “Most relevant”, Covidence will display the studies that are most likely to be included first. This helps teams move relevant studies through to the full-text review stage sooner.
The “Most relevant” sort uses machine learning (active learning) to identify trends in your team’s past screening behaviour on the review to determine and display the studies that are most likely to be included first. The more studies you screen, the stronger the system’s prediction will be:
Miwa 2014 describes how the active learning algorithm works and the benefits of using it to support screening.
Given the machine learning uses patterns in your team’s past screening behaviour to determine the relevancy, it won’t begin to use the predicted relevancy scores to sort until at least 25 studies have been marked as included or excluded, with at least 2 of those studies being included, and 2 of those studies being excluded.
Due to technical limitations, the “Most relevant” sort feature will not work as expected for reviews with over 150,000 studies. For these reviews, studies will order based on the order they were imported into Covidence.
The author sort option will display the studies in alphabetical order, based on the studies’ author.
The title sort option will display the studies in alphabetical order, based on the studies’ titles. This sorting approach is helpful during the piloting phase, as it can ensure reviewers see the same studies to screen first.
The most recent sort option will display the studies that have been recently imported first, allowing you to focus on what’s recently been added to the review on Covidence.
If you have any feedback or suggestions on how we can improve how we sort studies for the title and abstract screening, please let us know here.