Evidence map›Paper›PMID 41267746›Full record

ReviewJournal of multidisciplinary healthcare2025

Global Trends in Neurosurgical Nursing Research from 2014 to 2024: A Bibliometric and Visualization Study.

Wenhui Li, Xiang-Kong Song

Abstract readReview
In one paragraph

Review in Journal of multidisciplinary healthcare, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Wenhui LiDepartment of Neurosurgery, The Central Hospital of Qinghe County, Xingtai, Hebei, People's Republic of China.
Xiang-Kong SongDepartment of Neurosurgery, The Central Hospital of Qinghe County, Xingtai, Hebei, People's Republic of China.ORCID 0009-0007-3089-7444

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As neurosurgical nursing continues to evolve alongside technological advancements, a systematic examination of its research landscape has become increasingly imperative. This study employed bibliometric analysis to investigate global research trends in neurosurgical nursing from 2014 to 2024, utilizing 5677 records extracted from the Web of Science Core Collection (WOSCC). Through the application of bibliometric techniques and CiteSpace visualization tools, we quantitatively analyzed publication outputs, geographical distributions, institutional contributions, and keyword evolution patterns. Our temporal analysis revealed three key findings: (1) a steady annual growth in publication volume (average increase of 12.3% per year), with the United States contributing 39.90% of total publications; (2) dominant institutional contributors including Harvard University, the University of California System, and the University of Toronto, which collectively accounted for 28.5% of high-citation publications; and (3) emerging research foci centered on seven primary themes: clinical interventions ("clinical article", "external ventricular drain"), patient populations ("neurosurgical patients"), treatment modalities ("radiotherapy"), evidence synthesis ("meta analysis"), care delivery models ("patterns"), and anatomical considerations ("central nervous system"). These findings provided empirical evidence for understanding current research priorities, identifying knowledge gaps, and forecasting future developmental trajectories in neurosurgical nursing. The study established a foundational bibliometric framework that may guide strategic research planning and international collaboration in this specialized nursing field.

Indexed as

bibliometric analysisneurosurgerynursingtext-mining study

Identifiers

PMID41267746
PMCPMC12628780

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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.