Analysis of citation trends to identify articles on delirium worth reading using DDPP model with temporal heatmaps (THM): A bibliometric analysis

Abstract

Background: Delirium is one of the most common geriatric syndromes in older patients, accounting for 25% of hospitalized older patients, 31 to 35% of patients in the intensive care unit, and 8% to 17% of older patients in the emergency department (ED). A number of articles have been published in the literature regarding delirium. However, it is unclear about article citations evolving in the field. This study proposed a temporal heatmap (THM) that can be applied to all bibliographical studies for a better understanding of cited articles worth reading. Methods: As of November 25, 2022, 11,668 abstracts published on delirium since 2013 were retrieved from the Web of Science core collection. Research achievements were measured using the CJAL score. Social network analysis was applied to examine clusters of keywords associated with core concepts of research. A THM was proposed to detect articles worth reading based on recent citations that are increasing. The 100 top-cited articles related to delirium were displayed on an impact beam plot (IBP). Results: The results indicate that the US (12474), Vanderbilt University (US) (634), Anesthesiology (2168), and Alessandro Morandi (Italy) (116) had the highest CJAL scores in countries, institutes, departments, and authors, respectively. Articles worthy of reading were highlighted on a THM and an IBP when an increasing trend of citations over the last 4 years was observed. Conclusion: The THM and IBP were proposed to highlight articles worth reading, and we recommend that more future bibliographical studies utilize the 2 visualizations and not restrict them solely to delirium-related articles in the future.

Visual impact beam plots: Analyzing research profiles and bibliometric metrics using the following-leading clustering algorithm (FLCA)

“…The conventional IBPs possess distinct characteristics, [5][6][7][8][9] which include each article is represented by a dot on the plot, the rows correspond to publication years, while the columns represent normalized citations, ranging from 0% to 100% based on the research discipline, and by clicking on a dot, the associated article can be accessed and read online. The improved version of IBPs is anticipated to offer a more comprehensive research profile by incorporating additional information such as h-index and x-index, [10][11][12][13] self-citation rate, [14] citation network along with article clusters (e.g., timezone view on cooccurrence status of the keywords in the elite controller field [15] and landscape view of cocited reference cluster analysis [16] produced by CiteSpace software [17] ), and the representative article denoted by the weighted centrality degree (WCD) [18] among citing and cited articles (e.g., dual-map overlay of journals [19] using CiteSpace software [17] ).…”

Analyzing the number of articles with network meta-analyses using chord diagrams and temporal heatmaps over the past 10 years: Bibliometric analysis

“…As previously noted, the DDPP model [31,32] provides guidelines for physicians and researchers to identify key features that distinguish their field or discipline by means of DS, RD, RA, citation prediction, and articles worth reading. However, this model overlooks 3 important perspectives that require unique visualizations to effectively highlight relevant entities on a graph.…”

“…Searching PubMed for titles containing the phrase "100 topcited" revealed 433 publications. [33] It is common for bibliographical studies to refer to 3 types of information: descriptive statistics (DS), significant topics or article types with research domains (RD), and research achievements in entities (RA) [19] (i.e., descriptive and diagnostic analytics in the DDPP model [31,32] ). Furthermore, citation prediction [34][35][36][37] has been employed in bibliometrics to predict article citations based on weighted scores of article keywords, and THM has been applied to display articles worth reading.…”

“…Furthermore, citation prediction [34][35][36][37] has been employed in bibliometrics to predict article citations based on weighted scores of article keywords, and THM has been applied to display articles worth reading. [31,32,38] In this manner, the DDPP model can be used to conduct a comprehensive bibliometric analysis of MA articles.…”

“…Additional barriers and challenges are encountered by authors, including assigning article themes to countries of origin, comparing NMA and non-NMA articles, and conducting a comprehensive bibliometric analysis of MA articles’ characteristics. Although previous studies have illustrated theme extraction via coword analysis, [18,29] comparison of citation achievements in bibliographic and meta-analysis studies, [30] and use of the descriptive, diagnostic, predictive, and prescriptive analytics model (DDPP), [31,32] there has been no analysis of the trend comparison between NMA and non-NMA articles with visualizations (such as chord diagrams and temporal heatmaps [THM]) during the past 10 years.…”