The Alvarado Score is the Most Impactful Diagnostic Tool for Appendicitis: A Bibliometric Analysis

Publikation: Bidrag til tidsskriftReviewForskningfagfællebedømt

Introduction
The objective of this bibliometric analysis was to investigate the citation pattern of studies that have developed a diagnostic tool to diagnose appendicitis.

Methods
We investigated characteristics of citations, publication frequency, evolution of citations, and fluctuation of previously highly cited studies. We analyzed which studies had been cited in the method section and identified impactful studies in this research field by a network visualization. We analyzed the differences in citations between diagnostic tools requiring a doctor to be present against the diagnostic tools not requiring doctors to be present, English language studies against non-English studies, and identified diagnostic tools targeting children.

Results
There was an upward trend in publications in this research field, and between 1999-2021 the Alvarado score has been cited the most. In general, there was a high fluctuation, and 40 studies had been cited in the methods sections. There were significant differences in studies regarding diagnostic tools written in English compared to non-English studies, with more citations in the English-language studies. Furthermore, 22 studies had children as the target population.

Conclusions
The Alvarado score was the highest cited study since 1999, with 1086 citations, making it the most impactful study in this research field of diagnostic tools to diagnose appendicitis. Due to the diversity of target populations and settings for which diagnostic tools are developed, there is a need to expand research on diagnostic tools for appendicitis.
OriginalsprogEngelsk
TidsskriftJournal of Surgical Research
Vol/bind291
Sider (fra-til)557-566
Antal sider10
ISSN0022-4804
DOI
StatusUdgivet - 2023

Bibliografisk note

Funding Information:
This study was enabled by The Lens (www.lens.org) through a scholar API access.

Publisher Copyright:
© 2023 Elsevier Inc.

ID: 370117643