Conducting systematic reviews can be a daunting task, especially when faced with thousands of citations. Abstrackr, developed by Brown University’s Center for Evidence Synthesis in Health, offers a free, open-source solution to simplify this process. Designed to semi-automate the citation screening phase, Abstrackr leverages machine learning to predict the relevance of studies based on titles and abstracts, significantly reducing the manual workload.
Key Features of Abstrackr
- AI-Powered Recommendations: Utilize machine learning algorithms to identify and prioritize relevant citations, allowing researchers to focus on the most pertinent studies.
- Collaborative Screening: Invite team members to participate in the screening process, facilitating efficient collaboration and ensuring consistency across reviews
- Flexible Data Export: Export screening data in RIS or CSV formats, enabling seamless integration with other tools and systems.
- Mobile Optimization: Access and screen citations on-the-go with Abstrackr’s mobile-friendly interface, ensuring productivity from anywhere
- Real-Time Analytics Dashboard: Monitor screening progress and team performance through an interactive dashboard, providing valuable insights into the review process.
Why Choose Abstrackr?
Abstrackr appears to stand out as a reliable tool for researchers seeking to enhance the efficiency and accuracy of their systematic reviews. Its combination of AI-driven recommendations, collaborative features, and user-friendly interface makes it an invaluable asset for evidence synthesis. By adopting Abstrackr, research teams can streamline their workflow, reduce manual effort, and focus on what truly matters—conducting high-quality, comprehensive reviews.
If you have tried it, do share your views! Visit www.abstrackr.com
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