ArticleLasers in medical science2025
Research hotspots and trends of pediatric bone age: A bibliometric and visualization analysis.
Article in Lasers in medical science, 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.
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Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
purposeResearch related to pediatric bone age has gained substantial scholarly attention over recent decades, given its critical importance in monitoring growth and guiding clinical decision-making in children. This study aims to identify research hotspots and forecast future trends in the field of bone age assessment through bibliometric and visualization analyses.
methodA comprehensive bibliometric analysis was conducted using the Web of Science Core Collection to retrieve publications related to pediatric bone age from 1965 to 2024. Visualization analyses were performed utilizing VOSviewer (version 1.6.20), CiteSpace (version 6.3.R1), and the R package "bibliometrix."
resultsA total of 4,652 publications on pediatric bone age were subjected for analysis, with an annual growth rate of 8.09%. The United States emerged as the leading contributor, accounting for 27.7% of the total publications, followed by China and Italy. Harvard University was identified as the most productive institution, with 328 publications, and the Journal of Clinical Endocrinology & Metabolism was the most influential journal in the field. The top three frequently occurring keywords included "growth," "age," and "therapy." Keyword bursts revealed emerging keywords such as "deep learning" and "artificial intelligence."
conclusionThis bibliometric analysis identified that the future research within this area is expected to focus on integrating deep learning and artificial intelligence to improve diagnostic accuracy and clinical applications. CLINICAL TRIAL NUMBER: Not applicable.
Indexed as
Identifiers
41044432What 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.