Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Comparison of Altmetrics with Conventional Bibliometrics in the Surgical Literature (Preprint)
0
Zitationen
2
Autoren
2021
Jahr
Abstract
<sec> <title>BACKGROUND</title> The impact of a research publication has traditionally been quantified by its citation count. Newer bibliometric indices such as Altmetric Attention Score (AAS) and article page views are emerging as supplementary measures to quantify the academic influence of research. </sec> <sec> <title>OBJECTIVE</title> The aim of the current study was to interrogate the relationship between novel and traditional bibliometric indices for research published in a leading surgical journal and evaluate the role of these newer indices in measuring the impact of surgical research. </sec> <sec> <title>METHODS</title> All articles published in JAMA Surgery between 1 January 2019 and 1 September 2021 were examined. The literature database PubMed was used to identify all articles published within the specified time period. The cumulative citation count, AAS, and article page views were retrieved from the journal website. Statistical analysis using the Pearson rank correlation coefficient (r) was performed on Minitab 19. </sec> <sec> <title>RESULTS</title> : A total of 1,071 articles were retrieved for further analysis. The correlation (95% CI) between ranks for all articles was 0.396 (0.344-0.445) for AAS and citation scores, 0.541 (0.497-0.582) for citations and article page views, and 0.413 (0.362-0.461) for AAS and article page views. </sec> <sec> <title>CONCLUSIONS</title> We demonstrated a medium correlation between citations and AAS for articles published in a leading surgical journal. The inter-year correlation between 2019 and 2021 was similar, suggesting that AAS could be predictive of future citations. AAS may be useful in evaluating the wider societal impact of the surgical literature and could serve to promote greater public engagement in surgical research. </sec>
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.485 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.371 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.827 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.549 Zit.