A Bibliometric Analysis of Platelet Derivate Uses in Oral and Maxillofacial Surgery

Fardi, Anastasia; Kodonas, Konstantinos; Gogos, Christos

doi:10.1016/j.joms.2023.09.010

Cited by 4 publications

(2 citation statements)

References 39 publications

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Section: Discussionmentioning

confidence: 99%

“…The CPCA, a novel approach in this field, addresses a crucial research gap identified in the literature on bibliometrics and cluster analysis, particularly in the comparison of cluster patterns based on coordinates in collaboration (or thematic) maps. [53,54] Prior studies have focused on various clustering algorithms, such as K-Means Clustering [1] and Hierarchical Clustering, [2] which have been applied in diverse contexts, including medical analysis [11] and epidemiological studies. [12] However, the application of these methodologies in bibliometrics, particularly for comparing article entity cluster patterns across 2 publication periods, has been largely unexplored.…”

Section: Additional Informationmentioning

confidence: 99%

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Developing a novel algorithm for comparing cluster patterns in networks on journal articles during and after COVID-19: Bibliometric analysis

Wu,

Chow

2024

Medicine

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Background: Cluster analysis is vital in bibliometrics for deciphering large sets of academic data. However, no prior research has employed a cluster-pattern algorithm to assess the similarities and differences between 2 clusters in networks. The study goals are 2-fold: to create a cluster-pattern comparison algorithm tailored for bibliometric analysis and to apply this algorithm in presenting clusters of countries, institutes, departments, authors (CIDA), and keywords on journal articles during and after COVID-19. Methods: We analyzed 9499 and 5943 articles from the Journal of Medicine (Baltimore) during and after COVID-19 in 2020 to 2021 and 2022 to 2023, sourced from the Web of Science (WoS) Core Collection. Follower-leading clustering algorithm (FLCA) was compared to other 8 counterparts in cluster validation and effectiveness and a cluster-pattern-comparison algorithm (CPCA) was developed using the similarity coefficient, collaborative maps, and thematic maps to evaluate CIDA cluster patterns. The similarity coefficients were categorized as identical, similar, dissimilar, or different for values above 0.7, between 0.5 and 0.7, between 0.3 and 0.5, and below 0.3, respectively. Results: Both stages displayed similar trends in annual publications and average citations, although these trends are decreasing. The peak publication year was 2020. Similarity coefficients of cluster patterns in these 2 stages for CIDA entities and keywords were 0.73, 0.35, 0.80, 0.02, and 0.83, respectively, suggesting the existence of identical patterns (>0.70) in countries, departments, and keywords plus, but dissimilar (<0.5) and different patterns (<0.3) found in institutes and 1st and corresponding authors, during and after COVID-19. Conclusions: This research effectively created and utilized CPCA to analyze cluster patterns in bibliometrics. It underscores notable identical patterns in country-/department-/keyword based clusters, but dissimilar and different in institute-/author- based clusters, between these 2 stages during and after COVID-19, offering a framework for future bibliographic studies to compare cluster patterns beyond just the CIDA entities, as demonstrated in this study.

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Section: Discussionmentioning

confidence: 99%