Predicting substantive biomedical citations without full text.

ABSTRACT: Insights from biomedical citation networks can be used to identify promising avenues for accelerating research and its downstream bench-to-bedside translation. Citation analysis generally assumes that each citation documents substantive knowledge transfer that informed the conception, design, or execution of the main experiments. Citations may exist for other reasons. In this paper, we take advantage of late-stage citations added during peer review because these are less likely to represent substantive knowledge flow. Using a large, comprehensive feature set of open access data, we train a predictive model to identify late-stage citations. The model relies only on the title, abstract, and citations to previous articles but not the full-text or future citations patterns, making it suitable

SUBMITTER: Hoppe TA 

PROVIDER: S-EPMC10372685 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature