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

Wang, Hsien-Yi; Chien, Tsair‐Wei; Wang, Chenyu; Chou, Willy

doi:10.1097/md.0000000000030375

Cited by 8 publications

(13 citation statements)

References 49 publications

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“…These results are consistent with our findings regarding the dominance of the United States in articles regarding CHPR-related UTI since 2013 (hT-JIF = 38.30). [ 33 ]…”

Section: Discussionmentioning

confidence: 99%

“…The hT-JIF index is derived from Equations 3 to 5 if the number of articles is greater than 1. [33] Weight…”

Section: Statistical Description 231 Computation Of the Ht-jif Index ...mentioning

confidence: 99%

“…[30] The hT-index more closely related to the h-index than other bibliometric indices (e.g., x-/g-index [31,32] ) has been verified. [33] Time-to-event analysis, also known as survival analysis, refers to a set of methods for analyzing the length of time until a well-defined endpoint occurs. [34] It is unique to survival data that not all subjects (e.g., CHPR articles in this study) experience the event (e.g., having JIF denoted by 1 for the event) by the end of the observation period (e.g., JIF observed in this study) so that the exact survival times (denoted by JIF) for some articles are unknown (i.e., JIF = 0 censored with nonevent in time-to-event analysis).…”

Section: Challenges Faced In Comparing the 2 Issuesmentioning

confidence: 99%

“…These results are consistent with our findings regarding the dominance of the United States in articles regarding CHPR-related UTI since 2013 (hT-JIF = 38.30). [33] 4. An article with PMID = 24166342, [57] entitled History of Childhood Kidney Disease and Risk of Adult End-Stage Renal Disease, written by Ronit Calderon-Margalit (Israel) and published in N Engl J Med.…”

Section: Additional Informationmentioning

confidence: 99%

“…[ 30 ] The hT-index more closely related to the h-index than other bibliometric indices (e.g., x-/g-index [ 31 , 32 ] ) has been verified. [ 33 ]…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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 (χ 2 = 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.

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“…These results are consistent with our findings regarding the dominance of the United States in articles regarding CHPR-related UTI since 2013 (hT-JIF = 38.30). [ 33 ]…”

Section: Discussionmentioning

confidence: 99%

“…The hT-JIF index is derived from Equations 3 to 5 if the number of articles is greater than 1. [33] Weight…”

Section: Statistical Description 231 Computation Of the Ht-jif Index ...mentioning

confidence: 99%

Section: Challenges Faced In Comparing the 2 Issuesmentioning

confidence: 99%

Section: Additional Informationmentioning

confidence: 99%

“…[ 30 ] The hT-index more closely related to the h-index than other bibliometric indices (e.g., x-/g-index [ 31 , 32 ] ) has been verified. [ 33 ]…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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

et al. 2023

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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.

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

Chien

Lin

et al. 2023

Medicine

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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.

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

Cited by 8 publications

References 49 publications

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

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

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

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

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