ArticleJournal of medical Internet research2021
Status Quo and Research Trends of Neurosurgical Departments in China: Bibliometric and Scientometric Analyses.
Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundModern neurosurgery is a relatively young discipline characterized by finesse and complexity. In recent years, neurosurgery in China has made continuous developments, with long-term progress and outstanding discoveries in many aspects of the field.
objectiveThis scientometric investigation aimed to comprehensively provide insight into the development trends of neurosurgery in China, to demonstrate how the field has evolved.
methodsPubMed database was searched to retrieve relevant papers published between 1988 and 2018 from neurosurgery institutions in China. The database of the National Natural Science Foundation of China was also retrieved for funding information. Information (eg, year of publication, journal, institute of origin) and keywords were collected from each paper after removing duplicates and filtering unintentional words. Co-word analysis was performed on the papers' keywords, and a time distribution matrix of coexisting keywords in a given paper (ie, termed co-words) was established. Co-words were clustered according to their growth rate within years and visually presented with a mountain plot and a heatmap. Trends and potential subspecialties were identified, and each topic, represented either by a co-word from publications or funding from the National Natural Science Foundation of China during the period from 2011 to 2018, was collected and analyzed.
resultsWithin 15,972 publications on neurosurgery from institutions in China, diagnostic image was found to coexist the most with other keywords. Cluster 0, represented by diagnostic image with retrospective study, contained emerging topics with great developmental potential and demonstrated high growth rates in recent years. This finding suggests that the topics represented in Cluster 0 may represent future areas of important neurosurgical research. We also found that the developmental trend of China's neurosurgical research is highly correlated with National Natural Science Foundation of China funding acquisition.
conclusionsCo-word analysis and visualization results provided insight into the emerging research topics that are of vital importance, which can be used as a reference by neurosurgeons and researchers for future investigations. In this study, our analysis strategy based on co-word biclustering was able to clearly demonstrate current academic subject development; therefore, co-word biclustering is a reliable bibliometric analysis strategy.
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What 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.