Predicting substantive biomedical citations without full text

Travis A. Hoppe, Salsabil Arabi and B. Ian Hutchins ()
Additional contact information
Travis A. Hoppe: a Office of the Director, National Center for Health Statistics, Centers for Disease Control and Prevention , Hyattsville , MD 20782
Salsabil Arabi: b Information School, School of Computer, Data, and Information Sciences, College of Letters and Science, University of Wisconsin-Madison , Madison , WI 53706
B. Ian Hutchins: b Information School, School of Computer, Data, and Information Sciences, College of Letters and Science, University of Wisconsin-Madison , Madison , WI 53706

Proceedings of the National Academy of Sciences, 2023, vol. 120, issue 30, e2213697120

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 for publications as soon as they are released, or those behind a paywall (the vast majority). We find that high prediction scores identify late-stage citations that were likely added during the peer review process as well as those more likely to be rhetorical, such as journal self-citations added during review. Our model conversely gives low prediction scores to early-stage citations and citation classes that are known to represent substantive knowledge transfer. Using this model, we find that US federally funded biomedical research publications represent 30% of the predicted early-stage (and more likely to be substantive) knowledge transfer from basic studies to clinical research, even though these comprise only 10% of the literature. This is a threefold overrepresentation in this important type of knowledge flow.

Keywords: science policy; machine learning; citation analysis; artificial intelligence; bench to bedside translation (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1073/pnas.2213697120 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nas:journl:v:120:y:2023:p:e2213697120

Access Statistics for this article

More articles in Proceedings of the National Academy of Sciences from Proceedings of the National Academy of Sciences
Bibliographic data for series maintained by PNAS Product Team ().