SynthesisJournal of robotic surgery2025
Suggestions and insights from 28 bibliometric studies in the Journal of Robotic Surgery.
Synthesis in Journal of robotic surgery, 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
11 authors.
Funding
Abstract
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.
Indexed as
Identifiers
41441923What 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.