Why You Should Use Tagging Hierarchies?

What are the advantages of Tagging Hierarchies?

Tagging Hierarchies have several advantages over ‘flat’ tags, and Nested Knowledge has built on top of this to provide additional, unique capabilities in the review process:

1. Hierarchies structure relationships: Without hierarchies, applying tags to underlying records has limited value, since it only helps you figure out ‘is X concept here, yes or no?’ Searching for general concepts, drilling down on related ideas, and displaying the relationships among them is made possible by hierarchies.
2. Hierarchies make reading full texts easier: By building a hierarchy in advance, you are setting yourself up to easily search and apply tags in underlying studies. Either by structuring a query directly into the hierarchy, or by using our Tagging Recommendations to automatically jump to the concepts of interest in the full text, your concept identification can follow the structure you lay out in study design.
3. Hierarchies build institutional knowledge: Every time you build a hierarchy in Nested Knowledge, you can turn it into a Hierarchy Template and share it with your organization. That way, you never need to rebuild the same hierarchy, and can customize from the templates to reflect the specific research question(s) of interest.
4. Hierarchies enable data visualization: Once you have built your hierarchy and applied tags to underlying studies, the very fact that you provided the structure from the start enables Nested Knowledge to automatically create your data visualizations. For example, in the Basilar Artery Stroke study above, the authors published the Qualitative Synthesis visual embedded below. This visual helps your users drill down on concepts of interest simply by filtering your hierarchy; it also allows you to add Insights that guide your researchers to key concepts and studies!
5. Hierarchies enhance replicability of science: Scientific experts have long recognized that replicability is vital to experimentation. A key methodological step in designing studies is identifying endpoints, and to ensure replicability, many organizations (led by the NIH) have stressed the importance of Common Data Elements (CDEs). CDEs are trial endpoints that have identical definitions, timepoints, and measurement techniques across related studies, thus enabling apples-to-apples comparisons of interventions reporting CDEs. The same Stanford collaborators actually published a review that employed the Nested Knowledge hierarchy to determine the level to which current trials report similar endpoints in chronic subdural hematoma interventions, and noted that before this tool, choosing endpoints in any trial would lack any structured method of examining existing commonly-reported data elements. Thus, with hierarchies, you can build out the set of common concepts and bring your discipline toward replicable reporting of outcomes.