Core Smart Tags is an AI Extraction tool for commonly extracted elements in research projects: Population, Interventions/Comparators, & Outcomes (PICOs), Study Type, Study Location, and Study Size.
There are several advantages of Core Smart Tags:
- Immediate categorization of all studies when you add a Search, which can assist with iterative search strategy or screening
- Automatic Tag hierarchy generation and extraction
- Additional visuals for drilling down on interpreting your evidence
The tool currently extracts from Study Abstracts and is incorporated in several parts of the AutoLit workflow including Search Exploration for exploring these elements based on custom concepts, Tagging for auto-generated recommendations and extraction of these elements, and Dashboard for display of these data as cards.
Search Exploration #
Search Exploration allows you to input concepts, explore the literature and generate a search query to finalize in your nest. To assist in the exploration and query building, after inputting your concepts, view the most commonly reported PICOs, view a Study Type sunburst diagram, a Study Location choropleth/map (see below screenshot), a Study Size histogram, and an Acronym dictionary.
Interacting with each of these Exploration pages, you’ll be able to view associated records on the right hand side where you can do a deeper dive of specific records’ abstracts.
Note: While these cannot be directly incorporated into the search query, once the search is finalized, filters can be applied in Study Inspector to view and apply bulk actions to subsets of studies!
Tagging/Data Extraction #
Once a search has been finalized and records have been imported into your nest, Core Smart Tags switch from being your Search strategy tool to providing structure and time savings to your data extraction.
1. Input Research Question and Select Concepts #
Core Smart Tags for data extraction begins in the Tag Hierarchy. You can input a research question to help guide the AI as well as select from what concepts you’d like to be extracted from these studies. First navigate to the Configure Tag Hierarchy page, and select the “Core” button (red).
In the modal, input your research question, and choose from the options of PICOs, Study Type, Location and Size to be extracted in your studies (these are all selected by default but may be unchecked/checked depending on your preference). If you decide not to select one now, don’t worry, you can still come back to add them later or refresh existing ones.
2. Recommendations vs. Auto-Extraction #
By default, the tag hierarchy will be built and data will be displayed alongside studies as recommendations (shown alongside Smart Tag Recommendations). This way, a user can go through the individual studies and accept or reject the AI recommendations. To do this leave the “As Recommendations” box checked.
Alternatively, you may wish to bypass this step and have the Core Smart Tags be applied immediately. Note: these are applied to ALL studies, regardless of inclusion or exclusion so proceed with caution! You may wish to save a Nest Version or create a Nest Copy before proceeding. To apply all Core Smart Tags as they are generated, uncheck the “As Recommendations” box.
3. View the Hierarchy #
This auto-generates a hierarchy of concepts for you. Each tag comes into your nest pre-configured with a name, content type, and in some cases, child tags arranged hierarchically beneath them. By default, each root Core Smart Tag will be configured as a question. Additionally, every aspect of Core Smart Tags can be modified just like any other tag. This means you can add things like question type, descriptions, aliases, etc. to a Core Smart Tag or a Core Smart Tag’s child.
Note: editing Core Smart Tags does not change the content extracted by the AI.
Additionally, while Core Smart Tags generate recommendations or applied tags as soon as you add them to your hierarchy, you can of course override the individual applications in cases where the Core Smart Tags failed to find the correct answer.
4. View Recommendations and/or Extracted Data #
Core Smart Tags are generated using a custom-built LLM for pre-determined questions and extracts data from Abstracts only; it is different to Smart Tag Recommendations, which uses OpenAI GPT 4 for custom questions for Abstracts and Full Texts.
If you generated CST as recommendations, you can view each of them anywhere you can view Tagging:
To view CST as and when they are applied:
- Filter to these concepts on Study Inspector (in below example, Tag: Randomized Controlled Trial and Tag Contents: Study Location: UK)
- View Qualitative Synthesis Outputs (included studies only)
- Rapidly export collected data (either via Inspector or Dashboard, see below)
In all, generated Core Smart Tags are a fantastic way to scope out an initial SLR, conduct a rapid review, and more!
Dashboard #
After tags have been applied, you may wish to present your data as was available in Search Exploration. In Dashboard Editor, you can add a “Tag” card to display:
- Tags as sunburst diagrams
- Tag: Study Location as a choropleth/map
- Tag: Study Size as a histogram
