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SWIFT-Active Screener is a web-based, collaborative systematic review software application. Active Screener was designed to be easy-to-use, incorporating a simple, but powerful, graphical user interface with rich project status updates. What makes Active Screener special, however, is its behind-the-scenes application of state-of-the-art statistical models designed to save screeners time and effort by automatically prioritizing articles as they are reviewed, using user feedback to push the most relevant articles to the top of the list.

SWIFT-Active Screener Graphic

How Active Screener Works for You

As screening proceeds, reviewers include or exclude articles while an underlying statistical model automatically computes which of the remaining unscreened documents are most likely to be relevant. This “Active Learning” model is continuously updated during screening, improving its performance with each article reviewed. Meanwhile, a separate statistical model estimates the number of relevant articles remaining in the unscreened document list. Together, the combination of the two models allows users to screen relevant documents sooner and provides them with accurate feedback about their progress. Using this approach, the vast majority of relevant articles can often be discovered after reviewing only a fraction of the total number of articles. This can result in a significant time and cost savings for you and your team, especially for large projects.

For more information about SWIFT-Active Screener and other Sciome products and services, please contact us at swift-activescreener@sciome.com.

GET ACTIVE SCREENER

To use Active Screener, please contact us at swift-activescreener@sciome.com

NEWS

Sciome was honored to participate in the Society of Toxicology 57th Annual Meeting and ToxExpo on March 11-15, 2018 in San Antonio, TX. Our team’s work to create new tools and methods to automate and accelerate systematic review was featured in a number of presentations, including several posters that highlight SWIFT-Review, SWIFT-Active Screener, and our rapid Evidence Mapping (rEM) efforts. It was a pleasure to meet many of our active users in person and to learn more about your systematic review projects!

User-Friendly Project Creation

Hit the ground running! Quickly and easily create screening projects, including data extraction questions to fit most any study design, without the need to sit through hours of tutorial videos or read dozens of pages in a user manual.

Real Time Team Collaboration

Active Screener allows teams to collaborate using a web-based interface which will enable your group to perform a systematic review anywhere, anytime. This flexibility allows for easy progress sharing and makes team communication easier to manage.

Project Progress

Active Screener’s project management tools make it easy to view your team’s progress as your systematic review is underway.

Conflict Resolution

Active Screener tracks screening conflicts and allows you to resolve them quickly and efficiently so that you can complete your review without delay.

Data Integration

Active Screener integrates with all of the systematic review tools that you already use. It accepts imports from many of the most popular bibliographic databases and reference curation platforms including EndNote, Mendeley, Zotaro, PubMed, and SWIFT-Review. Results from screening activities in Active Screener can also be exported in standard data formats compatible with a wide range of applications including EndNote, Mendeley, Zotaro, PubMed, HAWC and Excel.

Publications and Presentations

  • Lam J, Howard B, Thayer, K. Shah R (2018). Low-Calorie Sweeteners and Health Outcomes: A Demonstration of rapid Evidence Mapping (rEM). Environment International (under review).
  • Howard BE, Tandon A, Phillips J, Shah MR, Mav D, Shah RR (2018). “Using Machine Learning and SWIFT-Active Screener to Reduce the Expense of Evidence Based Toxicology.” Poster presentation at the Society of Toxicology’s 57th Annual Meeting and ToxExpo, San Antonio, TX.
  • Howard BE, Tandon A, Maharana A, Shah RR (submitted). “SWIFT-Active Screener: accelerated document screening through Active Learning and integrated recall estimation.” (In preparation).
  • Glynn C, Tokdar ST, Howard B, Banks, D (2018). “Bayesian Analysis of Dynamic Linear Topic Models.” Bayesian Analysis.
  • Howard BE, Tandon A, Phillips J, Shah MR, Mav D, Shah RR (2017). “Using Machine Learning and SWIFT-Active Screener to Reduce the Expense of Evidence Based Toxicology.” Poster presentation at the Genetics and Environmental Mutagenesis 2017 Annual Fall Meeting, RTP, NC
  • Howard BE, Miller K, Phillips J, Shah M, Mav D, Thayer K, Shah R (2017). “Enabling Evidence-Based Toxicology with SWIFT Active Screener.” Poster presentation at the Society of Toxicology’s 56th Annual Meeting and ToxExpo, Baltimore, MD.
  • Miller K, Howard BE, Phillips J, Shah MR, Mav D, Thayer K, Shah RR (2016). “SWIFT-Active Screener: Reducing Literature Screening Effort Through Machine Learning for Systematic Reviews.” 24th Cochrane Colloquium. Seoul, South Korea.
  • Howard BE, Miller K, Shah RR (2016). “SWIFT-Active Screener (A Brief Introduction).” National Institute of Environmental Health Sciences (NIEHS). Research Triangle Park, NC.
  • Miller K, Howard BE, Phillips J, Shah MR, Mav D, Thayer K, Shah RR (2016). “Updating Systematic Reviews with Active Learning.” 24th Cochrane Colloquium. Seoul, South Korea.
  • Miller K, Howard BE, Phillips J, Shah MR, Mav D, Shah RR (2016). “SWIFT-Active Screener: Reducing Literature Screening Effort Through Machine Learning for Systematic Reviews.” Poster presentation at the Society of Toxicology’s 55th Annual Meeting and ToxExpo, New Orleans, LA.