The model of descriptive, diagnostic, predictive, and prescriptive analytics on 100 top-cited articles of nasopharyngeal carcinoma from 2013 to 2022: Bibliometric analysis

Abstract

Background: Nasopharyngeal carcinomas (NPCs) are prevalent in southeast Asia. There is a need to systematically review the current trend and status of NPC research. However, most bibliometric analyses have tended to focus on descriptive and diagnostic analytics rather than predictive and prescriptive analyses. Thus, it is necessary to use the model of the 4 (called the descriptive, diagnostic, predictive, and prescriptive analytics [DDPP]) to derive insights from the data. This study aimed to apply the DDPP model to classify article themes and illustrate the characteristics of NPCs; compare NPC researcher achievements across countries, institutes, departments, and authors; determine whether the mean citations of keywords can be used to predict article citations; and highlight articles that are worthy of reading. Methods: The Web of Science Core Collection was searched for 100 top-cited articles and reviews related to NPCs published between 2013 and 2022. As part of Microsoft Office Excel 2019, Visual Basic for Applications was used to illustrate the number of publications and scientific productivity of authors over time and to generate network/temporal heatmaps, chord/Sankey diagrams, radar/impact beam plots, and scatter/pyramid charts about collaborations among countries. The DDPP model identifies institutions, authors, and hotspots of NPC research. The category, journal, authorship, and L-index (CJAL) score was applied to evaluate individual research achievements. Results: A total of 10,564 publications were extracted from Web of Science Core Collection and screened for 100 top-cited articles and reviews related to NPCs. Despite having the highest number of publications (36%), China lags slightly behind the US in CJAL scores. CJAL was higher at Sun Yat-Sen University, Radiat Oncol department, and author Jun Ma from China. The number of article citations was significantly correlated with the number of weighted keywords ( F = 1791.17; P < .0001). Six articles with significantly increasing citations over the last 4 years were recommended. Conclusion: This bibliometric study utilizes the DDPP model to analyze the scientific progress of NPC over the past decade. The whole genome is a hot topic that may prove to be a promising research area in the future. A temporal heatmap may serve as a tool for providing readers with articles that are worth reading, which could lead to additional research in bibliometrics.

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

“…The DDPP model was demonstrated in this study, which had been mentioned in the study. [12] A chord diagram [34,37] provides us with a clear view of the relationship between 2 or more entities (e.g., themes and clusters in Fig. 5, which is rare in bibliometric analysis).…”

“…Delirium is an entity that is seen in a variety of medical and surgical specialties. [6] Thus, a comprehensive understanding of the bibliometric analysis using the DDPP model [12] is essential to conducting delirium research, based on a method that depends on the questions we are asking and the information we are seeking from our dataset.…”

“…A variety of methods can be used to analyze data. Bibliometric analysis has rarely been used to analyze the publications in the area of delirium with a comprehensive perspective (e.g., using the DDPP model [12] including descriptive, diagnostic, predictive, and prescriptive analytics). Delirium is an entity that is seen in a variety of medical and surgical specialties.…”

Classification and citation analysis of the 100 top-cited articles on nurse resilience using chord diagrams: A bibliometric analysis

“…Healthcare specialists have used citation rank analysis to determine the most influential papers in their field, which include biological markers of diseases, [24][25][26] mental health, [27,28] medical education, [29,30] and machine learning in cancer research. [31,32] In the field of nurses' resilience, no studies have been conducted to identify the most influential papers, particularly using both approaches: analyzing prominent entities with a glance view and examining article keywords (i.e., Keywords Plus in Web of Science, WoS: Clarivate Analytics in Philadelphia, PA) for predicting article citations.…”

Analyzing fulminant myocarditis research trends and characteristics using the follower-leading clustering algorithm (FLCA): A bibliometric study

“…[18] It has proven successful in various fields of research. [19–21] While some research [12] has been conducted on the focus and thematic trends of fulminant myocarditis using CiteSpace [22] and VOSviewer, [23] there are certain limitations that need to be addressed in future studies. These limitations include the absence of cluster analysis to classify article themes and features, a lack of clarity in the author cocitation network related to FM, such as the utilization of timeline view, [24] time zone view, [25] and dual-map overlay of journals [26] using CiteSpace software, [17] and the absence of dashboard-type visualizations to provide readers with a comprehensive understanding of FM’s development over the past several decades.…”