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<p>The evaluation of authors in bibliometric studies has traditionally relied on straightforward quantitative indicators such as publication counts, total citations, and composite measures like the h-index. Although these metrics offer convenient and comparable ways to assess productivity, they do not fully capture the increasingly complex and collaborative nature of modern scientific work. This review examines how bibliometric studies published in the Journal of Robotic Surgery have presented “top authors” and suggesting more informative strategies for author-level assessment. A systematic review of all bibliometric articles published in the journal up to December 2025 was conducted. Studies were included if their abstracts contained the terms “bibliometric,” “top 100 most cited,” or “top 50 most cited,” and only original research and review articles were analyzed. Twenty-eight (n-28) studies met the criteria. Across these papers, the number of publications and total citations were the most frequently reported metrics, while indices such as the h-index, g-index, and m-index appeared far less consistently. More advanced composite indicators—including the HG-composite and Q2-index—were entirely absent, despite offering more balanced evaluations by integrating cumulative output, influence, and career duration. Co-authorship network analysis was comparatively underutilized in the reviewed studies. Such analyses can help reveal collaboration patterns, identify influential author clusters, and highlight central contributors or bridges connecting different subfields—insights that are not captured by citation-based measures alone. Incorporating network structures and cluster-level exploration could therefore provide a clearer understanding of authors’ roles and scholarly influence within robotic surgery research. Taken together, these findings suggest that current bibliometric studies primarily focus on basic productivity measures, with less frequent use of advanced metrics or analyses of collaboration patterns. Incorporating additional approaches—such as composite indicators, co-authorship networks, and research cluster profiling—could offer a more comprehensive and informative view of author contributions, helping to better reflect the complexity of scholarly impact in the field.</p>

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Suggestions and insights from 28 bibliometric studies in the Journal of Robotic Surgery

  • 期刊论文

  • 发表日期：

    2025年12月24日

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  • Siddig Ibrahim Abdelwahab,
  • Manal Mohamed Elhassan Taha,
  • Abdullah Farasani,
  • Jobran M. Moshi,
  • Ahmad Assiri,
  • Saeed Alshahrani,
  • Khaled A. Sahli,
  • Hussam M. Shubaily,
  • Marwa Qadri,
  • Amani Khardali,
  • Waseem Hassan

摘要

The evaluation of authors in bibliometric studies has traditionally relied on straightforward quantitative indicators such as publication counts, total citations, and composite measures like the h-index. Although these metrics offer convenient and comparable ways to assess productivity, they do not fully capture the increasingly complex and collaborative nature of modern scientific work. This review examines how bibliometric studies published in the Journal of Robotic Surgery have presented “top authors” and suggesting more informative strategies for author-level assessment. A systematic review of all bibliometric articles published in the journal up to December 2025 was conducted. Studies were included if their abstracts contained the terms “bibliometric,” “top 100 most cited,” or “top 50 most cited,” and only original research and review articles were analyzed. Twenty-eight (n-28) studies met the criteria. Across these papers, the number of publications and total citations were the most frequently reported metrics, while indices such as the h-index, g-index, and m-index appeared far less consistently. More advanced composite indicators—including the HG-composite and Q2-index—were entirely absent, despite offering more balanced evaluations by integrating cumulative output, influence, and career duration. Co-authorship network analysis was comparatively underutilized in the reviewed studies. Such analyses can help reveal collaboration patterns, identify influential author clusters, and highlight central contributors or bridges connecting different subfields—insights that are not captured by citation-based measures alone. Incorporating network structures and cluster-level exploration could therefore provide a clearer understanding of authors’ roles and scholarly influence within robotic surgery research. Taken together, these findings suggest that current bibliometric studies primarily focus on basic productivity measures, with less frequent use of advanced metrics or analyses of collaboration patterns. Incorporating additional approaches—such as composite indicators, co-authorship networks, and research cluster profiling—could offer a more comprehensive and informative view of author contributions, helping to better reflect the complexity of scholarly impact in the field.