Authors who contributed most to the fields of hemodialysis and peritoneal dialysis since 2011 using the hT-index: Bibliometric analysis

Analyzing collaboration and impact: A bibliometric review of four highly published authors' research profiles on collaborative maps

The concept of impact beam plots (IBPs) has been introduced in academia as a means to profile individual researchers. Despite its potential, there has been a lack of comprehensive analysis that evaluates the research profiles of highly published authors through the lens of collaborative maps. This study introduces a novel approach, the rating scale for research profiles (RSRP), to create collaborative maps for prolific authors. The initial hypothesis posited that each of the research profiles would attain a grade A, necessitating empirical verification. This research employed collaborative maps to analyze the publication patterns of authors using the Web of Science database, focusing on co-authorship patterns and the impact of their scholarly work. The study relied on various bibliometric indicators, such as publication count, citation metrics, h-index, and co-authorship networks, to provide a detailed assessment of the contributions made by each author in their field. Additionally, authors' IBPs were generated and assessed alongside collaborative maps, using a grading scale ranging from A (excellent) to F (lacking any articles as first or corresponding author). The analysis confirmed that all 4 research profiles achieved a grade A, with their centroids located in the third quadrant, indicating a high level of scholarly impact. The h-indexes for the authors were found to be 38, 51, 53, and 59, respectively. Notably, Dr Tseng from Taiwan showed a distinct pattern, with a significant number of solo-authored publications in the second quadrant, in contrast to the other 3 authors who demonstrated a greater emphasis on collaboration, as evidenced by their positioning in the first quadrant. The study successfully demonstrates that RSRP and IBPs can be effectively used to analyze and profile the research output of highly published authors through collaborative maps. The research confirms the initial hypothesis that all 4 profiles would achieve a grade A, indicating an excellent level of scholarly impact and a strong presence in their respective fields. The utility of collaborative maps can be applied to bibliometric indicators in assessing the contributions and impact of scholars in the academic community.

Using chord diagrams to explore article themes in 100 top-cited articles citing Hirsch's h-index since 2005: A bibliometric analysis

BACKGROUND

The h-index is increasingly being used as a measure of individual research achievement (IRA). More than 4876 citing articles have been published and indexed in Web of Science. The articles citing the h-index that have made the greatest contribution to scientific academics are still unknown. It is also unclear which subject categories (SCs) can be classified based on their keywords.

METHODS

These 4976 citing articles have been collected from the Web of Science since 2005. SCs were classified using chord diagrams to visualize their associations of SCs and documents in 100 top-cited articles (T100hciting). In addition to chord diagrams, 6 visualizations were used to illustrate study results: choropleth maps were used to depict the geographical distribution of publications across countries, network diagrams were created by using coword analysis, box plots were created to complement the network diagrams, Sankey diagrams highlighted the 5 most important elements in each article entity, the dot plot was used for displaying T100hciting, and a radar plot was used to present the top 10 high-IRA elements of countries, institutes, departments, and authors based on category, journal impact factor, authorship, and L-index scores.

RESULTS

A coword cluster analysis indicates that the majority of articles come from the US (918, 18%) and China (603, 12%), the top 2 SCs are h-index and bibliometric analysis, and the top 5 countries account for 55% in T100hciting, such as the US (25%), Spain (10%), Netherlands (9%), China (6%), and Belgium (5%). In T100hciting, 4 SCs are included, namely, the h-index (72%), bibliometric analysis (24%), physics & multidisciplinary (3%), and infectious diseases (1%).

CONCLUSION

A total of 7 visualizations were used to display the results in this study. Chord diagrams are suggested as a tool for future bibliographical studies to classify SCs Future bibliometrics with chord diagrams should not be limited to the topic of h-index-citing articles, as we did in this study.

An app for predicting patient dementia classes using convolutional neural networks (CNN) and artificial neural networks (ANN): Comparison of prediction accuracy in Microsoft Excel

BACKGROUND

Dementia is a progressive disease that worsens over time as cognitive abilities deteriorate. Effective preventive interventions require early detection. However, there are no reports in the literature concerning apps that have been developed and designed to predict patient dementia classes (DCs). This study aimed to develop an app that could predict DC automatically and accurately for patients responding to the clinical dementia rating (CDR) instrument.

METHODS

A CDR was applied to 366 outpatients in a hospital in Taiwan, with assessments on 25 and 49 items endorsed by patients and family members, respectively. The 2 models of convolutional neural networks (CNN) and artificial neural networks (ANN) were applied to examine the prediction accuracy based on 5 classes (i.e., no cognitive decline, very mild, mild, moderate, and severe) in 4 scenarios, consisting of 74 (items) in total, 25 in patients, 49 in family, and a combination strategy to select the best in the aforementioned scenarios using the forest plot. Using CDR scores in patients and their families on both axes, patients were dispersed on a radar plot. An app was developed to predict patient DC.

RESULTS

We found that ANN had higher accuracy rates than CNN with a ratio of 3:1 in the 4 scenarios. The highest accuracy rate (=93.72%) was shown in the combination scenario of ANN. A significant difference was observed between the CNN and ANN in terms of the accuracy rate. An available ANN-based app for predicting DC in patients was successfully developed and demonstrated in this study.

CONCLUSION

On the basis of a combination strategy and a decision rule, a 74-item ANN model with 285 estimated parameters was developed and included. The development of an app that will assist clinicians in predicting DC in clinical settings is required in the near future.

A leading bibliometric author does not have a dominant contribution to research based on the CJAL score: Bibliometric analysis

BACKGROUND

A total of 22,367 bibliometric articles have been indexed by Web of Science (WoS). The most significant contribution to the field has not yet been identified through bibliometric analysis. A comparison of individual research achievements (IRAs) and trend analysis of article citations are required after extracting bibliometric articles. The study aimed to confirm whether the leading author has a dominant RA and which articles are worth reading for readers using trend analysis.

METHODS

We identified authors with at least 100 articles related to bibliometrics in the WoS core collection. A total of 399 articles were collected to cluster author collaborations. Co-word analysis and chord diagrams were used to match chief authors in clusters with Keywords Plus in WoS core collection. The category, journal impact factor, authorship, and L-index (CJAL) score and the absolute advantage coefficient (AAC) were used to compare IRAs and identify the leading author who dominated the field significantly beyond the next 2 authors. In addition to network charts and chord diagrams, 4 visualizations were used to report study results, including a Sankey diagram, a dot plot, a temporal trend graph, and a radar plot. The temporal bubble graph was used to select articles that deserve to be read.

RESULTS

The top 3 authors were Lutz Bornmann, Yuh-Shan Ho, and Giovanni Abramo, with CJAL scores of 176.22, 176.02, and 112.06, respectively, from Germany, Italy, and Taiwan. Based on the weak dominance coefficient (AAC = 0.20 < 0.70), it is evident that the leading bibliometric author has no such significant power beyond the next 2 leading authors in IRAs. A trend analysis of the last 4 years was used to illustrate the 2 articles that deserve to be read.

CONCLUSION

Three leading authors were identified through a co-word analysis of bibliometrics. There was no evidence of an author who possessed a dominant position due to a lower AAC on the leading author. The CJAL score and the AAC can be applied to many bibliographical studies in the future rather than being limited to bibliometric studies that evaluate the leading authors in a field, as we did in this study.

Research features between Urology and Nephrology authors in articles regarding UTI related to CKD, HD, PD, and renal transplantation

BACKGROUND

A urinary tract infection (UTI) is one of the most common types of infections affecting the urinary tract. When bacteria enter the bladder or kidney and multiply in the urine, a URI can occur. The urethra is shorter in women than in men, which makes it easier for bacteria to reach the bladder or kidneys and cause infection. A comparison of the research differences between Urology and Nephrology (UN) authors regarding UTI pertaining to the 4 areas (i.e., Chronic Kidney Disease, Hemodialysis, Peritoneal Dialysis, and Renal Transplantation [CHPR]) is thus necessary. We propose and verify 2 hypotheses: CHPR-related articles on UTI have equal journal impact factors (JIFs) in research achievements (RAs) and UN authors have similar research features (RFs).

METHODS

Based on keywords associated with UTI and CHPR in titles, subject areas, and abstracts since 2013, we obtained 1284 abstracts and their associated metadata (e.g., citations, authors, research institutes, departments, countries of origin) from the Web of Science core collection. There were 1030 corresponding and first (co-first) authors with hT-JIF-indices (i.e., JIF was computed using hT-index rather than citations as usual). The following 5 visualizations were used to present the author's RA: radar, Sankey, time-to-event, impact beam plot, and choropleth map. The forest plot was used to distinguish RFs by observing the proportional counts of keyword plus in Web of Science core collection between UN authors.

RESULTS

It was observed that CHPR-related articles had unequal JIFs (χ2 = 13.08, P = .004, df = 3, n = 1030) and UN departments had different RFs (Q = 53.24, df = 29, P = .004). In terms of countries, institutes, departments, and authors, the United States (hT-JIF = 38.30), Mayo Clinic (12.9), Nephrology (19.14), and Diana Karpman (10.34) from Sweden had the highest hT-JIF index.

CONCLUSION

With the aid of visualizations, the hT-JIF-index and keyword plus were demonstrated to assess RAs and distinguish RFs between UN authors. A replication of this study under other topics and in other disciplines is recommended in the future, rather than limiting it to UN authors only, as we did in this study.