Investigating clinical links in edge-labeled citation networks of biomedical research: A translational science perspective

Abstract

While clinical citations have been widely used as the preeminent measure of the clinical impact of biomedical paper, there has been a scarcity of in-depth studies exploring their temporal and structural characteristics, as well as its influence on the clinical translation. To fill this gap, we categorized biomedical papers and their citations into four groups from the translational science perspective: basic, clinical, mixed, and human-related. Subsequently, we constructed an edge-labeled citation network and four clinical translation networks. Our analysis encompassed 114,342 papers in the field of Alzheimer's Disease, accompanied by 5,161,626 citations, of which 2.77 % were clinical citations, 18.77 % basic citations, 41.85 % mixed citations, and 36.61 % human-related citations. First, utilizing time- and structure-randomized networks, we conducted a quantitative analysis of clinical citations' incidence patterns, impact assortativity, temporal occurrence patterns, and temporal co-location patterns throughout the lifecycles of biomedical research. Second, in comparison to control groups, we evaluated the short- and long-term impacts of different types of citations on the academic influence and clinical translation of biomedical research. Our findings reveal that clinical citations effectively bolster the academic influence of biomedical papers, and this positive effect appears to amplify over time. Conversely, while basic, mixed, and human-related citations may initially aid in the clinical translation of biomedical research, over 70 % of them exhibit an inhibitory effect on clinical translation in the long run. These findings afford us a deep and specific understanding of how clinical citations operate within the context of biomedical papers, thereby serving as a crucial guide for effectively promoting the clinical translation of biomedical research.