Dataset Information

Overview of Sankey flow diagrams: Focusing on symptom trajectories in older adults with advanced cancer.

ABSTRACT: This perspectives paper provides an overview of how to read and interpret a Sankey, examples using symptom data from older adults with advanced cancer, a synopsis of medical literature, and comments on creating and using the diagram for presentation of data. From prior reports and our own, we conclude Sankeys are an excellent tool for visualizing the changing status of older patients with cancer. Older adult symptom data is used as an example with data dispalyed in a range of Sankey flow diagrams. Because there is large heterogeneity in aging, different subgroups can be examined. In a single diagram, Sankey can show both the likelihood and variability of patients' future status from their current status.

SUBMITTER: Otto E

PROVIDER: S-EPMC9232856 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Overview of Sankey flow diagrams: Focusing on symptom trajectories in older adults with advanced cancer.

Otto Ethan E Culakova Eva E Meng Sixu S Zhang Zhihong Z Xu Huiwen H Mohile Supriya S Flannery Marie A MA

Journal of geriatric oncology 20220107 5

This perspectives paper provides an overview of how to read and interpret a Sankey, examples using symptom data from older adults with advanced cancer, a synopsis of medical literature, and comments on creating and using the diagram for presentation of data. From prior reports and our own, we conclude Sankeys are an excellent tool for visualizing the changing status of older patients with cancer. Older adult symptom data is used as an example with data dispalyed in a range of Sankey flow diagram ...[more]

PMID: 35000890

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Project description:BackgroundAge-related macular degeneration (AMD) is the primary cause of vision impairment in older adults, especially in developed countries. While many articles on AMD exist in the literature, none specifically delve into the trends based on document categories. While bibliometric studies typically use dual-map overlays to highlight new trends, these can become congested and unclear with standard formats (e.g., in CiteSpace software). In this study, we introduce a unique triple-map Sankey diagram (TMSD) to assess the evolution of AMD research. Our objective is to understand the nuances of AMD articles and show the effectiveness of TMSD in determining whether AMD research trends have shifted over the past decade.MethodsWe collected 7465 articles and review pieces related to AMD written by ophthalmologists from the Web of Science core collection, accumulating article metadata from 2014 onward. To delve into the characteristics of these AMD articles, we employed various visualization methods, with a special focus on TMSD to track research evolution. We adopted the descriptive, diagnostic, predictive, and prescriptive analytics (DDPP) model, complemented by the follower-leading clustering algorithm (FLCA) for clustering analysis. This synergistic approach proved efficient in identifying and showcasing research focal points and budding trends using network charts within the DDPP framework.ResultsOur findings indicate that: in countries, institutes, years, authors, and journals, the dominant entities were the United States, the University of Bonn in Germany, the year 2021, Dr Jae Hui Kim from South Korea, and the journal "Retina"; in accordance with the TMSD, AMD research trends have not changed significantly since 2014, as the top 4 categories for 3 citing, active, and cited articles have not changed, in sequence (Ophthalmology, Science & Technology - Other Topics, General & Internal Medicine, Pharmacology & Pharmacy).ConclusionThe introduced TMSD, which incorporates the FLCA algorithm and features in 3 columns-cited, active, and citing research categories-offers readers clearer insights into research developments compared to the traditional dual-map overlays from CiteSpace software. Such tools are especially valuable for streamlining the visualization of the intricate data often seen in bibliometric studies.

Project description:Systematic review methods are recognized for their rigor and transparency and are widely adapted to frameworks that cover review types such as systematic reviews, scoping reviews, and systematic evidence maps. Reporting guidelines help promote better systematic review practices and detailed documentation of the review process for different types of health research (e.g., PRISMA-Preferred Reporting Items for Systematic Reviews and Meta-Analyses; CONSORT-Consolidated Standards of Reporting Trials; and STROBE-Strengthening the Reporting of Observational Studies in Epidemiology). Transparency in the systematic review process and reporting of results is one of the key advantages of the methods and particularly important for hazard and risk assessments due to the high level of scrutiny these reviews face from scientific, political, and public communities. Data visualizations are important to clearly convey information from a review by helping readers perceive, understand, and assess the displayed information easily and quickly. The study flow diagram is a required element of a systematic review and maps out the number of included and excluded records identified, and the reasons for exclusion. Static literature flow diagrams help viewers readily understand the general review methodology and summarize the number of records included or excluded at each stage of the review. However, such diagrams can be time-consuming to develop and maintain during a systematic review or scoping review, and they provide limited summary-level information. We explored how the use of online systematic review tools such as DistillerSR coupled with visualization software such as Tableau can efficiently generate an Interactive REFerence Flow (I-REFF) diagram that is linked to the literature screening data, thus requiring minimal preparation, and resulting in a simplified process for updating the diagram. Furthermore, I-REFF diagrams enhance transparency and traceability by not only summarizing the records in the review but also allowing viewers to follow specific records throughout the review process. We present an example I-REFF diagram and discuss recommendations for key interactive elements to include in these diagrams and how this workflow can improve efficiency and result in an accessible and transparent interactive literature flow diagram without advanced programming.

Project description:BackgroundHidradenitis suppurativa (HS) is a chronic, inflammatory and debilitating dermatosis characterized by painful nodules, sinus tracts and abscesses in apocrine gland-bearing areas that predominantly affect women worldwide. New therapeutic interventions based on the clinical manifestations of patients have recently been introduced in numerous articles. However, which countries, journals, subject categories, and articles have the ultimate influence remain unknown. This study aimed to display influential entities in 100 top-cited HS-related articles (T100HS) and investigate whether medical subject headings (i.e., MeSH terms) can be used to predict article citations.MethodsT100HS data were extracted from PubMed since 2013. Subject categories were classified by MeSH terms using social network analysis. Sankey diagrams were applied to highlight the top 10 influential entities in T100HS from the three aspects of publication, citations, and the composited score using the hT index. The difference in article citations across subject categories and the predictive power of MeSH terms on article citations in T100HS were examined using one-way analysis of variance and regression analysis.ResultsThe top three countries (the US, Italy, and Spain) accounts for 54% of the T100HS. The T100HS impact factor (IF) is 12.49 (IF = citations/100). Most articles were published in J Am Acad Dermatol (15%; IF = 18.07). Eight subject categories were used. The "methods" was the most frequent MeSH term, followed by "surgery" and "therapeutic use". Saunte et al, from Roskilde Hospital, Denmark, had 149 citations in PubMed for the most cited articles. Sankey diagrams were used to depict the network characteristics of the T100HS. Article citations did not differ by subject category (F(7, 92) = 1.97, P = .067). MeSH terms were evident in the number of article citations predicted (F(1, 98) = 129.1106; P < .001).ConclusionWe achieved a breakthrough by displaying the characteristics of the T100HS network on the Sankey diagrams. MeSH terms may be used to classify articles into subject categories and predict T100HS citations. Future studies can apply the Sankey diagram to the bibliometrics of the 100 most-cited articles.

Dataset Information

Overview of Sankey flow diagrams: Focusing on symptom trajectories in older adults with advanced cancer.

Publications

Overview of Sankey flow diagrams: Focusing on symptom trajectories in older adults with advanced cancer.

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