ReviewMedicine2025
Using social network analysis (SNA) and the performance sheet to explore decision support tools in adult long-term care facilities and author contributions to the field of Geriatrics & Gerontology: Bibliometric analysis.
Review in Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA number of reviews have been published concerning clinical decision support systems in long-term care facilities (DSSLTCF). These reviews frequently utilize literature analysis to evaluate the characteristics of DSSLTCF. However, none of the existing research has employed social network analysis (SNA) to classify their features concerning digital innovations aimed at mitigating staff shortages and improving quality. To better understand the current landscape of DSSLTCF, it is crucial to examine the tools currently in use. The objectives of this review are 2-fold: to classify DSSLTCF using cluster analysis, and to identify the authors who have significantly contributed to DSSLTCF research in recent years.
methodsLiterature published since 2010 was reviewed using key search terms in the Web of Science Core Collection. The review focused solely on articles and review articles that evaluated DSSLTCF. Cluster analysis was performed using SNA, with evidence provided by the similarity in proportional counts of major keywords between groups. A performance sheet was used to illustrate the top 10 contributing entities (including countries, institutes, departments, and authors) to DSSLTCF based on the h-index.
resultsA total of 69 papers were included in the final review, divided into 2 groups: target papers (n = 16) and contrast papers (n = 43). There was no significant difference in proportional counts for major keywords between the 2 groups. Nine themes of DSSLTCF were identified, including digital technology. The 4 entities contributing the most to DSSLTCF with the highest publication counts were: the United States (25), the University of Wisconsin (4), Medicine (6), and Christine R, Kovach from the US (3) in the categories of countries, institutes, departments, and authors, respectively.
conclusionsThe use of SNA and h-indexes is a viable and effective method for classifying and identifying DSSLTCF. This study demonstrates the visualization of DSSLTCF characteristics, including their classifications and authors' contributions, and recommends these methods for future research beyond the scope of DSSLTCF.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.