Evidence map›Paper›PMID 40874123›Full record

SynthesisFrontiers in neurology2025

Efficacy of traditional Chinese medicine nursing in stroke: a systematic review and meta-analysis.

Yanling Chen, Hongyan Li, Zhe Wu, Mingyuan Jiao

Abstract readSystematic Review
In one paragraph

Synthesis in Frontiers in neurology, 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

4 authors.

Yanling ChenNursing Department, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China.
Hongyan LiNursing Department, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China.
Zhe WuRehabilitation Department, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China.
Mingyuan JiaoResearch and Teaching Department, Jinhua Maternal and Child Health Care Hospital, Jinhua, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Some clinical studies have suggested that traditional Chinese medicine (TCM) nursing techniques may promote better recovery in stroke patients compared to conventional neurological nursing. However, high-quality evidence-based research is still lacking. This study selected two outcome indicators-motor function and activities of daily living (ADL)-to systematically evaluate the efficacy of TCM nursing techniques in the rehabilitation of stroke patients. Methods: Seven major Chinese and English databases were systematically searched. The Fugl-Meyer Assessment (FMA) and the Modified Ashworth Scale (MAS) were used to evaluate motor function, while the Barthel Index (BI) was used to assess ADL. Subgroup analyses were conducted based on study characteristics to explore potential sources of heterogeneity. Results: A total of 18 studies involving 1,419 patients were included. Meta-analysis results showed that, compared with conventional care, the TCM nursing group demonstrated significantly better outcomes in FMA (SMD = 1.92, Conclusion: Compared to conventional care, TCM nursing techniques can significantly improve spasticity in stroke patients, and also provide some benefits for limb motor function and ADL. Given the high heterogeneity and poor GRADE assessment of FMA and BI, the above results should be interpreted with caution. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD420251006204.

Indexed as

activities of daily livingmeta-analysismotor functionnursingstroketraditional Chinese medicine

Identifiers

PMID40874123
PMCPMC12378047

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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.