Tag Archives: covidence

Choosing Between Extraction 1 and Extraction 2 in Covidence

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Here’s a tip for getting the most out of data extraction in Covidence.

For background, the MSK Library has an institutional account to Covidence, an online software platform used for systematic and other related reviews. Covidence offers teams a collaborative space to screen, appraise, and extract data from articles, and our institutional account means anyone at MSK can use this platform for their review projects.

Once you’re within the Covidence page for your review, you’ll see there are four stages below Review Summary, with Extraction at the end. When you click on Settings to the right of Review Summary, you’ll have the option of selecting between Extraction 1 and Extraction 2.

Both extraction options offer a customizable data extraction template, so which to choose?

Covidence offers the FAQ: How to decide when to use Extraction 1 vs Extraction 2.

  • Extraction 1 is designed for intervention reviews with a standardized PICO(T) structure, as it offers a structured format for organized data collection, which makes meta-analysis easier. This structure allows it to automatically fill in data extraction fields with suggestions you can review. Results can be exported to CSV, Excel, and RevMan.
  • Extraction 2 offers an unstructured format for flexible data collection and is fully customizable. It doesn’t offer automated extraction suggestions and only exports to CSV.

Learn more about data extraction and templates for these two options in the Covidence Knowledge Base. If you prefer to be hands-on, Covidence offers a demo review, and you can test both extraction options there before choosing which one is best for your project.

Learn more about reviews, Covidence, and the way MSK librarians can support you within the guide to our Systematic Review Service.

Covidence Full-Text PDF Enhancements

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Covidence, the systematic review (SR) project management tool that is available for use by everyone in the MSK community via a site license, has recently introduced some noteworthy improvements related to importing the PDF attachments for the full-text screening stage.

  • You can now export only the records of citations missing the Full-Text PDFs.

Being able to easily extract a list of citations of studies missing full text PDFs does not sound like a game-changing enhancement. However, for SR team members who regularly dedicate hours of work harvesting the full-text PDFs for the Full-Text Screening stage of a project, this functionality will be a huge time-saver.

  • You can now export citations as an RIS file for import into reference manager programs beyond Endnote, including:

    1. Endnote
    2. Cochrane Registry of Studies
    3. Zotero
    4. Mendeley
    5. RefWorks

With an increasing number of citation manager program options available to users, including here at MSK – see Citation Management LibGuide for more details – it is nice to see that Covidence is becoming inter-operable with more tools to better match user preferences.

  • You can now bring in Open Access articles using Unpaywall and automatically upload them when studies move to to the Full Text screening or Data Extraction stages.

With the amount of available Open Access journal content continuously increasing, having the ability to automatically bring in the needed full-text that is openly-available on the web will become even more useful as time goes on. Furthermore, individual PDFs that users have already downloaded can now be brought into Covidence using a convenient drag and drop option, making adding these PDFs to their corresponding citation records on the screening list easier than ever.

  • You can now use the Bulk PDF Upload Tool in conjunction with either Endnote or Zotero.

The Bulk PDF Upload Tool involves a two step process that includes saving a list of citation records as an Endnote XML file after all of the needed PDFs have been harvested into a citation manager. Zotero now also accommodates the “Endnote XML” filetype, helping Covidence once again become more versatile for users who choose to use the Zotero citation manager instead of Endnote.

To learn more about Covidence, be sure to check out these training options or Ask Us at the MSK Library.

Cochrane RCT Classifier

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As anyone who has worked on a systematic review (SR) project can attest – the record screening process can be a frustratingly tedious and time-consuming one. If available, most reviewers would likely welcome some kind of automation that streamlines and potentially reduces the manual record screening portion of their SR workload.

What is an RCT classifier algorithm?

An RCT classifier algorithm is “a tool to help you sort out the non-RCTs so that you can focus your effort on studies more likely to be included in your review”. In other words, researchers working on SRs that specify in their protocol that only studies reporting on RCTs will be included can now take advantage of tools that help them predict – using an automated algorithm derived from machine learning – whether a study is using a possible RCT or a not an RCT study design. 

The research team behind the leading RCT classifier algorithm tool (which includes members of the EPPI-Centre and Cochrane) published a paper in May 2022 describing the development and evaluation of their tool:

Thomas J, McDonald S, Noel-Storr A, Shemilt I, Elliott J, Mavergames C, Marshall IJ. Machine learning reduced workload with minimal risk of missing studies: development and evaluation of a randomized controlled trial classifier for Cochrane Reviews. J Clin Epidemiol. 2021 May;133:140-151. doi: 10.1016/j.jclinepi.2020.11.003. Epub 2020 Nov 7. PMID: 33171275; PMCID: PMC8168828. 

The good news for our MSK community is that this RCT classifier functionality has already been incorporated into Covidence, the systematic review project management system that the MSK Library subscribes to and provides access to. To turn this function on in a review that they are working on, a team member will need to have first selected the “Medical and health sciences” option under the “Area of Research” drop-down menu. After choosing to create this kind of review, the option to “Automatically tag studies reporting on RCTs using the Cochrane RCT Classifier” will become visible for a user to decide to enable of not. If enabled (only works with titles that have >15 characters and abstracts that have >400 characters), their SR records will be tagged as “Possible RCT” or “Not RCT” and can be filtered accordingly.

Learn more about “How to tag studies not reporting on RCTs” by checking out this article from the Covidence knowledgebase.

Questions? Ask Us at the MSK Library.