The tool's AI model continuously learns from your screening decisions and re-orders the unscreened citation list, presenting the records it predicts as most relevant next.
Multiple reviewers can work on the same screening project simultaneously. The system tracks each user's decisions and prevents duplicate screening of the same record.
When two reviewers disagree on including or excluding a citation, the tool flags it for a third reviewer to adjudicate, all within the same interface.
Visual dashboards show overall progress, individual reviewer contributions, and statistics on screening decisions (includes, excludes, conflicts).
Supports importing citations from major databases via standard formats (RIS, PubMed XML). Allows exporting final decisions in CSV format for integration with reference managers and data synthesis software.
Medical researchers and clinical guideline developers use Abstrackr to screen thousands of PubMed citations for interventional studies. The AI prioritization helps them identify the handful of relevant RCTs much faster, allowing them to proceed to meta-analysis and guideline development sooner. This is critical for timely updates of evidence-based medical practices.
PhD students and faculty in fields like psychology or education conduct scoping reviews to map the literature on a broad topic. Abstrackr helps them manage the large, diverse sets of records from databases like PsycINFO or ERIC. The collaboration features allow student teams to divide the screening workload efficiently under supervisor oversight.
Research librarians assisting faculty with systematic reviews use Abstrackr as a recommended service. They help set up the project, train researchers on the tool, and sometimes participate as a second screener. This elevates the library's role from simple literature search to active research support.
Professors in public health or evidence-based medicine courses use Abstrackr for student assignments. Students get hands-on experience with the most tedious part of a review in a controlled, guided environment. The tool's transparency in showing AI-assisted prioritization also serves as a practical lesson in machine learning applications in research.
Policy analysts needing a quick synthesis of evidence to inform decision-making use Abstrackr to conduct rapid reviews. While not as exhaustive as full systematic reviews, the tool's efficiency allows them to rigorously screen a focused body of literature within tight deadlines, ensuring policy recommendations are still evidence-informed.
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