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Incorporating preprints in systematic reviews: a preliminary study of a novel method for rapid evidence synthesis
Jiayi Tong1,2,3, Yifei Sun4, Rebecca A Hubbard5
1The Center for Health AI and Synthesis of Evidence (CHASE), Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA 19104, United States.
Objectives:
By October 1, 2024, over 450,000 COVID-19 manuscripts were published, with 10% posted as unreviewed preprints. While they accelerate knowledge sharing, their inconsistent quality complicates systematic studies.
Materials And Methods:
We propose a 2-stage method to include preprints in meta-analyses. In Stage A, preprints are integrated through restriction or imputation and weighted by a confidence score reflecting their publication likelihood. In Stage B, we assess and adjust for potential publication or reporting biases.
Results:
This preliminary study employed a 2-stage procedure validated with 2 COVID-19 treatment case studies. For hydroxychloroquine, the relative risk (RR) was 1.06 [95% CI: 0.62, 1.80], suggesting no mortality benefit over placebo. For corticosteroids, the RR was 0.88 [95% CI: 0.62, 1.27], which, while not statistically significant, aligns with evidence supporting a mortality benefit.
Discussion:
Our research aims to bridge a significant methodological gap by providing a solution for timely evidence synthesis, particularly in the face of the overwhelming number of publications surrounding COVID-19.
Conclusion:
This preliminary study presents a method to efficiently synthesize COVID-19 research, including non-peer-reviewed preprints, to support clinical and policy decisions amidst the information surge.
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