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From bench to bedside: determining what drives academic citations in clinical trials

Author

Listed:
  • Zhifeng Liu

    (Peking University)

  • Chenlin Wang

    (Peking University)

  • Ruojia Wang

    (Beijing University of Chinese Medicine)

Abstract

Academic research translates into improved health outcomes by influencing clinical trials that lead to changes in clinical practice. However, the process of incorporating academic research into clinical trials remains underexplored. This study aims to dissect the mechanisms underlying the translation of academic scholarship into clinical practice, focusing on how academic articles are cited within clinical trials. To begin with, we employed logistic regression to scrutinize the impact of paper-related features and author-related features on whether a paper was cited in clinical trials to quantify the relationships between these features and citation decisions, offering an initial insight into the primary factors shaping clinical citations. Expanding on this analysis, we adopted the SHAP explainable framework to probe how paper- and author-related features affect the decision to cite academic papers in clinical trials. The analysis reveals that citations are predominantly swayed by features of the paper rather than those related to the author. Specifically, the academic impact of a paper, including whether it is among the top 10% in terms of total citations within its respective field and publication year and the number of citations within the first year after being published, assumes a pivotal role. Moreover, features such as the number of references, authors, the h-index of the first author and their affiliated institution, and the number of institutions could facilitate citation in clinical trials. However, an intriguing finding is that the relationship between disruption and clinical citation follows an inverted U-shaped pattern. Our study enhances the understanding of how research is integrated into clinical trials, offering valuable insights for elevating the translational potential of scholarly articles and facilitating their inspirational role in clinical application.

Suggested Citation

  • Zhifeng Liu & Chenlin Wang & Ruojia Wang, 2024. "From bench to bedside: determining what drives academic citations in clinical trials," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(11), pages 6813-6837, November.
    • Handle: RePEc:spr:scient:v:129:y:2024:i:11:d:10.1007_s11192-024-05173-2
    DOI: 10.1007/s11192-024-05173-2
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    References listed on IDEAS

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    1. Dongyu Zang & Chunli Liu, 2023. "Exploring the clinical translation intensity of papers published by the world’s top scientists in basic medicine," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(4), pages 2371-2416, April.
    2. Lingfei Wu & Dashun Wang & James A. Evans, 2019. "Large teams develop and small teams disrupt science and technology," Nature, Nature, vol. 566(7744), pages 378-382, February.
    3. Mike Thelwall & Kayvan Kousha, 2016. "Are citations from clinical trials evidence of higher impact research? An analysis of ClinicalTrials.gov," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 1341-1351, November.
    4. Mike Thelwall & Nabeil Maflahi, 2016. "Guideline references and academic citations as evidence of the clinical value of health research," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(4), pages 960-966, April.
    5. Wang, Jian & Veugelers, Reinhilde & Stephan, Paula, 2017. "Bias against novelty in science: A cautionary tale for users of bibliometric indicators," Research Policy, Elsevier, vol. 46(8), pages 1416-1436.
    6. Dongyu Zang & Chunli Liu, 2023. "Correction: Exploring the clinical translation intensity of papers published by the world’s top scientists in basic medicine," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(4), pages 2417-2418, April.
    7. Magnus Eriksson & Annika Billhult & Tommy Billhult & Elena Pallari & Grant Lewison, 2020. "A new database of the references on international clinical practice guidelines: a facility for the evaluation of clinical research," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(2), pages 1221-1235, February.
    8. Zhihong Huang & Qianjin Zong & Xuerui Ji, 2022. "The associations between scientific collaborations of LIS research and its policy impact," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6453-6470, November.
    9. O. Mryglod & Yu. Holovatch & R. Kenna, 2022. "Big fish and small ponds: why the departmental h-index should not be used to rank universities," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(6), pages 3279-3292, June.
    10. Feiheng Luo & Aixin Sun & Mojisola Erdt & Aravind Sesagiri Raamkumar & Yin-Leng Theng, 2018. "Exploring prestigious citations sourced from top universities in bibliometrics and altmetrics: a case study in the computer science discipline," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(1), pages 1-17, January.
    11. B Ian Hutchins & Matthew T Davis & Rebecca A Meseroll & George M Santangelo, 2019. "Predicting translational progress in biomedical research," PLOS Biology, Public Library of Science, vol. 17(10), pages 1-25, October.
    12. Yeon Hak Kim & Aaron D. Levine & Eric J. Nehl & John P. Walsh, 2020. "A bibliometric measure of translational science," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2349-2382, December.
    13. Liu, Jialin & Chen, Hongkan & Liu, Zhibo & Bu, Yi & Gu, Weiye, 2022. "Non-linearity between referencing behavior and citation impact: A large-scale, discipline-level analysis," Journal of Informetrics, Elsevier, vol. 16(3).
    14. Christopher Traylor & Christoph Herrmann-Lingen, 2023. "Does the journal impact factor reflect the impact of German medical guideline contributions?," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(3), pages 1951-1962, March.
    15. Li, Xin & Tang, Xuli & Cheng, Qikai, 2022. "Predicting the clinical citation count of biomedical papers using multilayer perceptron neural network," Journal of Informetrics, Elsevier, vol. 16(4).
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