Studies show a strong association between dementia and lower urinary tract symptoms (LUTS). The aim of this study was to investigate whether LUTS are a risk factor for cognitive impairment. We enrolled 50-year-old and older subjects with LUTS (LUTS[+]) (n = 6801) and controls without LUTS (LUTS[−]) (n = 20,403) from Taiwan's National Health Insurance Research Database. LUTS, dementia, and other confounding factors are defined by International Classification of Diseases, Ninth Revision, Clinical Modification Codes. Participants were recruited from 2000 to 2004 and then followed up until death or the end of 2011. The outcome was the onset of dementia, which was assessed using Poisson regression analysis, Cox hazards models, and Kaplan-Meier survival curves. The incidence of dementia was significantly higher in the LUTS[+] group than in the LUTS[−] group (124.76 versus 77.59/1000 person-years). The increased risk of dementia related to LUTS remained significant after adjustment for potential confounders (adjusted hazard ratio (AHR): 1.61, 95% confidence interval (CI) 1.47–1.76, P < 0.0001) and higher than that related to cerebrovascular disease (AHR: 1.43, 95% CI 1.26–1.61, P < 0.0001). The outcome suggests the need for early screening and appropriate intervention to help prevent cognitive impairment of patients with LUTS.

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.

Background: More than 400 articles with the title of 100 top-cited articles (Top100) have been published in PubMed. It is unknown whether their citations are fewer (or more) than those found in other bibliometric studies (Nontop100). After determining article themes using coword analysis, a temporal bubble graph (TBG) was used to verify the hypothesis that the Top100 had fewer citations than the Nontop100. Methods: Using the Web of Science core collection, the top 50 most cited articles were compiled by Top100 and Nontop100, respectively, based on the research area of biomedicine and bibliometrics only. Coword analysis was used to extract themes. The study results were displayed using 6 different visualizations, including charts with bars, pyramids, forests, clusters, chords, and bubbles. Mean citations were compared between Top100 and Nontop100 using the bootstrapping method. Results: There were 18 citations in total for the 2 sets of the 50 most cited articles (range 1–134; 5 and 26.5 for Top100 and Nontop100, respectively). A significant difference in mean citations was observed between the 2 groups of Top100 and Nontop100 based on the bootstrapping method (3, 95% confidence interval: [1.18, 4.82]; 26.5, 95% confidence interval: [23.82, 29.18], P < .001). The 11 themes were clustered using coword analysis and applied to a TBG, which is composed of 4 dimensions: themes, years, citations and groups of articles. Among the 2 groups, the majority of articles were published in the journal of Medicine (Baltimore), with 9 and 7, respectively. Conclusion: Eleven themes were identified as a result of this study. In addition, it reveals distinct differences between the 2 groups of Top100 and Nontop100, with the former containing more recently published articles and the latter containing more citations for articles. Clinical and research clinicians and researchers can use bibliometric analysis to appraise published literature and to understand the scientific landmark using TBG in bibliometrics.

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