Confidence score: a data-driven measure for inclusive systematic reviews considering unpublished preprints.

ABSTRACT:

Objectives

COVID-19, since its emergence in December 2019, has globally impacted research. Over 360 000 COVID-19-related manuscripts have been published on PubMed and preprint servers like medRxiv and bioRxiv, with preprints comprising about 15% of all manuscripts. Yet, the role and impact of preprints on COVID-19 research and evidence synthesis remain uncertain.

Materials and methods

We propose a novel data-driven method for assigning weights to individual preprints in systematic reviews and meta-analyses. This weight termed the "confidence score" is obtained using the survival cure model, also known as the survival mixture model, which takes into account the time elapsed between posting and publication of a preprint, as well as metadata such as the number of first 2-week c

SUBMITTER: Tong J 

PROVIDER: S-EPMC10990515 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature