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Integrating BERT and Graph Convolutional Networks for Medical Literature Mining: A Knowledge Graph Ap-proach to Pelvic Fracture Research Analysis

Ma, Y.; Wang, C.; Cui, G.; Li, Y.; Yue, C.; Wang, W.

2025-09-18 orthopedics
10.1101/2025.09.16.25335868 medRxiv
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BackgroundPelvic fractures have consistently been a focal point in orthopedic research. This study aims to provide a comprehensive analysis of the literature on pel-vic fractures published between 1983 and 2023, revealing research trends, hotspots, and frontiers in this field. ObjectiveThis study aimed to provide a comprehensive bibliometric and knowledge graph-based analysis of pelvic fracture literature published between 1983 and 2023, identifying research trends, hotspots, and emerging frontiers in this field. MethodsWe searched the Web of Science database using a predefined strategy restricted to review articles and original research articles, excluding studies outside orthopedics and surgery. Medical entities and relationships were extracted to construct a comprehensive knowledge graph. Entity recognition, relationship extraction, and network topology analyses were performed to map research evolution and collaboration networks. ResultsA total of 5248 articles were included for analysis. The results show a steady increase in the annual publication of pelvic fracture research, particularly after 2005. The United States, Germany, and China are the top three coun-tries in terms of the number of publications, with the University of Wash-ington, University of California, and University of San Francisco ranking the top three regions. Pohlmann T published the most significant number of ar-ticles, and Vaidya R was the strongest citation bursts author. Research on pelvic fractures has made significant progress over the past forty years, espe-cially in treatment techniques and methods. Bibliometric analysis reveals re-search hotspots in this field, such as hemostasis control, fracture fixation techniques, and osteoporotic fractures. Conclusions: This study employs bibliometrics to quantify and delineate the contemporary research landscape and trends in pelvic fracture research, aspiring to provide scholars with a compass for navigating the realm of pelvic fracture-related research.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.