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

Zhifeng Liu, Chenlin Wang and Ruojia Wang ()
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Zhifeng Liu: Peking University
Chenlin Wang: Peking University
Ruojia Wang: Beijing University of Chinese Medicine

Scientometrics, 2024, vol. 129, issue 11, No 13, 6813-6837

Abstract: 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.

Keywords: Clinical citation; Disruption; LightGBM; Explainable machine learning; SHAP (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11192-024-05173-2

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