Screening Automation for Systematic Reviews: A 5-Tier Prompting Approach Meeting Cochrane's Sensitivity Requirement

Systematic Reviews are essential for synthesizing evidence from multiple studies, but the process, particularly the title and abstract screening phase, is time-consuming and labour-intensive. Traditional machine learning methods for automating this phase often fall short of the sensitivity required by Cochrane, which is set at greater than 0.99. This paper introduces a novel 5-tier prompting approach leveraging a foundational Large Language Model to automate the screening process. First, each study is assigned to one of five classes based on its likelihood of meeting predefined inclusion and exclusion criteria. Using a specified threshold, these classifications are then converted into binary decisions. This approach minimizes the risk of excluding relevant papers while automatically excluding the majority of irrelevant ones.Evaluation conducted on 5,643 records from four published systematic reviews resulted in zero wrong excludes when compared to human full-text screening decisions. The executed experiments resulted in a 68% average reduction in human workload, which enables a 50% decrease in the time needed to complete the screening process, all without compromising the accuracy of the results. These findings suggest that the 5-tier prompting approach offers a promising solution for enhancing the efficiency of systematic reviews.