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Predicting substantive biomedical citations without full text.

Travis A Hoppe1, Salsabil Arabi2, B Ian Hutchins2

  • 1Office of the Director, National Center for Health Statistics, Centers for Disease Control and Prevention, Hyattsville, MD 20782.

Proceedings of the National Academy of Sciences of the United States of America
|July 18, 2023
PubMed
Summary
This summary is machine-generated.

Biomedical citation analysis can identify research acceleration. A new model distinguishes substantive knowledge transfer from peer review additions, revealing US federally funded research is threefold overrepresented in crucial basic-to-clinical knowledge flow.

Keywords:
artificial intelligencebench to bedside translationcitation analysismachine learningscience policy

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Area of Science:

  • Biomedical informatics
  • Bibliometrics
  • Research impact analysis

Background:

  • Biomedical citation networks offer insights into research acceleration and translation.
  • Current citation analysis often assumes all citations represent substantive knowledge transfer.
  • Citations can be added for reasons other than direct knowledge contribution, particularly during peer review.

Purpose of the Study:

  • To develop a predictive model to identify late-stage citations, often added during peer review.
  • To differentiate between substantive knowledge transfer and other citation types.
  • To quantify the contribution of US federally funded research to early-stage knowledge transfer.

Main Methods:

  • Utilized a comprehensive feature set of open access data.
  • Trained a predictive model using only article titles, abstracts, and previous citations.
  • The model avoids full-text analysis and future citation patterns for early applicability.

Main Results:

  • The model successfully identifies late-stage citations, including rhetorical ones like journal self-citations added during review.
  • Low prediction scores accurately flagged early-stage citations indicative of substantive knowledge transfer.
  • US federally funded research constitutes 30% of predicted substantive knowledge transfer from basic to clinical studies, despite being only 10% of the literature.

Conclusions:

  • The developed model effectively distinguishes substantive knowledge transfer from peer-review-influenced citations.
  • US federally funded biomedical research shows a significant overrepresentation in driving early-stage translational knowledge flow.
  • This finding highlights the disproportionate impact of US federal funding on advancing biomedical research from basic science to clinical application.