Evidence mapPaperPMID 42064880Full record

ArticleFrontiers in public health2026

Application scope of knowledge graphs in nursing: a scoping review.

Yu Wang, Xue Pan, Hanghang Jin, Rongyue Guo, Kechen Zhu, Wanying Zhao, Xin Wang

Abstract readScoping Review
In one paragraph

Article in Frontiers in public health, 2026. 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

7 authors.

Yu WangSchool of Nursing and Health, Zhengzhou University, Zhengzhou, China.
Xue PanSchool of Nursing and Health, Zhengzhou University, Zhengzhou, China.
Hanghang JinSchool of Nursing and Health, Zhengzhou University, Zhengzhou, China.
Rongyue GuoSchool of Nursing and Health, Zhengzhou University, Zhengzhou, China.
Kechen ZhuSchool of Nursing and Health, Zhengzhou University, Zhengzhou, China.
Wanying ZhaoSchool of Nursing and Health, Zhengzhou University, Zhengzhou, China.
Xin WangSchool of Nursing and Health, Zhengzhou University, Zhengzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: The application of knowledge graph technology in the healthcare field is increasingly in-depth, yet there is a lack of literature that systematically sorts out the overall research landscape of its application in nursing from a macro perspective. This study aimed to systematically depict the research panorama of knowledge graph applications in nursing, stratify existing studies by stages through the construction of an exploratory evidence maturity framework, reveal structural gaps and translation barriers, and provide insights for subsequent in-depth research and practical applications. Methods: This study adopted the Arksey scoping review reporting framework and followed the PRISMA-ScR checklist for reporting. We systematically searched databases including Wanfang, CNKI, VIP, SinoMed, PubMed, Embase, Web of Science, CINAHL, and the Cochrane Library, and summarized and analyzed the included articles. The research results were comprehensively collated and divided into five phases: the Construction Phase, System Performance Evaluation Phase, Usability Evaluation Phase, Preliminary Application Phase, and Application Phase. Results: A total of 30 studies were included, with methodological studies as the main research design ( Conclusion: Knowledge graph research in nursing is still in the exploratory stage, dominated by evidence on technical construction, with no objective validation of its clinical application effects. Future research should adopt an evidence-driven approach, focusing on clinical application, optimizing study design and advancing data standardization, thereby enabling knowledge graphs to deliver evidence-based support in nursing practice. Scoping review registration: https://osf.io/, identifier 10.17605/OSF.IO/F7SB5.

Indexed as

Knowledge BasesNursingHumansartificial intelligenceknowledge graphsnursingnursing informaticsscoping review

Identifiers

PMID42064880
PMCPMC13127119

What Socratic holds

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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 Socratic graph.