Fact Sheet

Better Quality Systematic Reviews - Faster

Systematic review teams face an enormous challenge to synthesize and analyze a tsunami of newly published scientific research every year. More than 3 million scientific articles are published in English every year, and the volume continues to grow by 8% to 9% annually.

With DistillerSR, academic researchers can effectively manage the growing volume of new research within their systematic literature reviews by leveraging AI-powered automation and intelligent workflows.

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Do More Reviews Faster and Smarter

DistillerSR automates many of the traditional review tasks so you can produce work quickly, accurately, and cost-effectively.


Skip End-of-Review Updates

Reviews are automatically updated with new references as soon as they become available by using auto-alerts from data providers, such as PubMed. Time spent re-running searches are eliminated and living reviews are always up-to-date with new references.


Easy-to-Use, Cost-Effective Full-Text Retrieval

Full-text documents can be automatically retrieved and added to systematic reviews. Systematic review teams can leverage their Article Galaxy and RightFind subscriptions from within DistillerSR for the lowest possible cost. Review teams can also instantly access via Unpaywall’s browser plugin free open source full-text documents.


Instant Access to Source Materials

DistillerSR is connected directly to source materials stored in your organizations’ e-Library and through DOI.org. This eliminates the tedious task of searching for and uploading full-text documents.

“If your search returns 100,000 references, DistillerSR can handle it. It makes my life a lot easier.”
Jeanette Andrade, Assistant Professor and Program Director, University of Florida

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Artificial Intelligence (AI) Reduces Screening Times and Improves Review Quality

DistillerSR’s AI-powered automation and intelligent workflows enable transparent systematic literature reviews faster and more accurately.


Duplicate Detection & Quarantine

Using powerful duplicate detection built within your workflow, DistillerSR easily identifies and removes duplicate records. The literature review software also automatically tracks their removal for standard reporting and preserves them for future reference and retrieval.


Find Relevant References Faster

With the help of AI, DistillerSR allows you to find what you need quickly and can easily sort through irrelevant materials. Its AI differentiates between relevant and irrelevant records, continuously reprioritizing the remaining, as-yet-unscreened records, and then presents them to reviewers based on the likelihood of relevance. On average, you can find most of your relevant references between 40–60 percent sooner than conventional screening. This allows you to start working on other stages of the review more quickly.


Prevent Erroneous Exclusions

DistillerSR’s AI can also confirm that you haven’t accidentally excluded any references – it does this by double-checking your exclusions for errors, increasing your confidence in your screening decisions. Conflicts and disagreements between reviewers, meanwhile, are automatically identified and set aside for easy resolution.

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Take Control of Your Reviews – The Way You Want

DistillerSR lets you define your preferred workflows. This approach streamlines your reviews, ensures reviewers follow best practices, and reduces logistical overhead for local and global teams. By following proven methodologies, you establish standard review processes and reduce opportunities for human error.


Create & Reuse Templates That Fit Your Protocol

The literature review software allows you to extract, appraise, and report data in a way that fits your review protocol. Furthermore, when you need to conduct similar research, you can instantly reuse your review process across multiple reviews by creating templates for other projects.


Eliminate Time Wasted Data Cleaning

DistillerSR helps to reduce human error by requiring data validation at the time of extraction, such as using pre-specifying acceptable ranges for numerical values. Configured to meet your requirements, DistillerSR’s in-form data validation lets your team spend less time and effort to acquire analysis-ready data.


Meet Global Standards Easily

You can also easily follow globally recognized standards and compliance requirements. This includes auto-generating PRISMA flow diagrams, maintaining strict version control, and producing results that are fully transparent and audit-ready.

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A Single Source of Trusted Evidence

DistillerSR facilitates a transparent process that tracks all project and account activity. In addition, it provides insights for the intelligent and automated management of your literature review.


Fully Transparent, Secure, & Audit Ready

DistillerSR allows project managers to create fully defensible, transparent, audit-ready, and reproducible reviews. The literature review software tracks all review activity and makes it easy to view the provenance of every cell of data. You can restrict access and ensure your data meets your organization’s security standards through Single Sign-On and user permissions.


Simplified, Real-Time Oversight

Distributed teams can collaborate more effectively, with standard processes in place and reduced management overhead. Reviewers are automatically assigned and notified of new work, regardless of the number of projects they are working on. Furthermore, all of their incomplete work is accessible from one central location. Project managers can view real-time user and project metrics to gain insight into their team workload, participation, quality, and performance.

Regardless of the subject matter, the size of your team, or how many reviews you have on the go, DistillerSR gives you the capabilties and flexibility you need to create a reliable, transparent, and fully reproducible process.

An article in BMC’s Medical Research Methodology reported that using DistillerSR reduced article screening burden by as much as five person-weeks on a single project1 – a significant time savings and reduction in cost associated with the systematic review.

1. Hamel, C., Kelly, S.E., Thavorn, K. et al. An evaluation of DistillerSR’s machine learning-based prioritization tool for title/abstract screening – impact on reviewer-relevant outcomes. BMC Med Res Methodol 20, 256 (2020). https://doi.org/10.1186/s12874-020-01129-1

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