Evidence map›Paper›PMID 40852529›Full record

SynthesisFrontiers in neurology2025

Value of temporal muscle thickness in predicting outcomes of stroke: a systematic review and meta-analysis.

Min Fang, Fangjun Wang, Yu Sheng, Shengchen Qiu

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. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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.

Min FangDepartment of Nursing, The Fourth Affiliated Hospital of School of Medicine, International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China.
Fangjun WangDepartment of Rehabilitation Medicine, The Fourth Affiliated Hospital of School of Medicine, International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China.
Yu ShengDepartment of Nursing, The Fourth Affiliated Hospital of School of Medicine, International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China.
Shengchen QiuDepartment of Rehabilitation Medicine, The Fourth Affiliated Hospital of School of Medicine, International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This systematic review examined if temporal muscle thickness (TMT) as a surrogate marker of sarcopenia was a predictor of outcomes after stroke. Methods: We explored the PubMed, Embase, Scopus, and Web of Science databases till 18th January 2025 for studies reporting stroke prognosis based on baseline TMT. Pooled analyses examined associations with post-stroke mortality, functional outcomes, and dysphagia. Meta-regression was conducted using baseline NIHSS and TMT values as moderators. Results: Nine studies were available. Meta-analysis showed that low TMT was associated with a statistically significant increase in the risk of post-stroke mortality (OR: 1.42 95% CI: 1.03, 1.96 Conclusion: Preliminary evidence suggests that lower TMT is associated with higher mortality after stroke, while associations with functional recovery remain inconclusive. Current limitations, including heterogeneity and lack of sex-specific analyses, preclude routine clinical use. Further standardized and patient-level research is warranted. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42025637925.

Indexed as

dysphagiafunctional outcomesmortalitysarcopeniaskeletal muscle thicknessstroke

Identifiers

PMID40852529
PMCPMC12369562

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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.