Application of Machine Learning Classifiers in Rapid Reviews for Health Research: A Case Example Using EPPI-Reviewer.

INTRODUCTION: Rapid reviews aim to deliver timely evidence for decision-makers when full systematic reviews are not possible or practical. Efficient selection of studies is challenging when questions are complex, or the evidence base is diffuse. METHODS: In a rapid review on trial informativeness, our team used EPPI-Reviewer, a web-based systematic review platform that supports document management, screening, and machine learning prioritization, to conduct title and abstract screening. We developed a machine learning classifier model within the platform to rank records by predicted relevance based on coding structures aligned with predefined criteria. RESULTS: The classifier model correctly concentrated relevant studies in the higher probability bands, which allowed most eligible records to be identified early. As screening progressed to lower probability bands, the number of newly identified records declined, indicating effective prioritization. Real-time collaboration and a clear audit trail supported consistent decision-making across reviewers. Limitations included the initial effort to train the model and potential subscription costs. CONCLUSION: Classifier assisted screening in EPPI-Reviewer improved the feasibility of conducting a rapid review on a complex topic within a limited timeframe. Although the risk of missed citations remains, this is inherent to any review method. With appropriate training and support, classifier models and platforms like EPPI-Reviewer can enhance both efficiency and transparency in rapid evidence synthesis.