Evidence map›Paper›PMID 37845368›Full record

ArticleScientific reports2023

Application of digitization and visualization-based muscle strength measurement in ischemic stroke patients with motor dysfunction.

Zhongyu Ren, Shangrong Ye, Qi Nie, Junlin Feng, Kaixiang Liu, Qinghua Li, Jian Wen

Abstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Trial
  2. Article
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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.

Zhongyu Ren *Nervous System Clinical Medicine Research Center, Affiliated Hospital of Guilin Medical University, Guilin, 541001, China.
Shangrong Ye *Nervous System Clinical Medicine Research Center, Affiliated Hospital of Guilin Medical University, Guilin, 541001, China.
Qi Nie *Nervous System Clinical Medicine Research Center, Affiliated Hospital of Guilin Medical University, Guilin, 541001, China.
Junlin FengDepartment of Neurology, Affiliated Hospital of Guilin Medical University, Guilin, 541000, China.
Kaixiang LiuDepartment of Neurology, Affiliated Hospital of Guilin Medical University, Guilin, 541000, China.
Qinghua LiDepartment of Neurology, Affiliated Hospital of Guilin Medical University, Guilin, 541000, China.
Jian WenNervous System Clinical Medicine Research Center, Affiliated Hospital of Guilin Medical University, Guilin, 541001, China. wenjian2400@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ischemic stroke stands as a prevalent neurological ailment, where a paucity of methodologies exists for the assessment of functional outcomes post-stroke. Our objective was the development of a WeChat applet for the evaluation of muscle strength and the subsequent evaluation of its validity in ischemic stroke patients experiencing motor dysfunction. The assessment encompassed Lovett and NIHSS, followed by muscle strength values (MSV) and muscle strength ratios (MSR). These metrics were then scrutinized in relation to NIHSS and Lovett, followed by examination of their interrelationships. We enrolled a cohort of 157 patients, with an average age of 65 years, consisting of 96 males and 61 females. Lovett scores in the range of 2-4 and NIHSS scores spanning from 0 to 3 were found to correspond to specific values of MSV and MSR, respectively. Upon conducting correlation analysis, we noted that both MSV and MSR exhibited significant positive correlations with Lovett scores and NIHSS. Remarkably, the correlation of MSR with Lovett scores or NIHSS surpassed that of MSV. The WeChat applet offers a means of digitization and visualization of muscle strength. It correlates well with Lovett score and NIHSS, especially MSR. This bears potential significance in guiding the rehabilitation of stroke patients.

Indexed as

Brain IschemiaIschemic StrokeStrokeAgedFemaleHumansMaleSeverity of Illness Index

Identifiers

PMID37845368
PMCPMC10579306

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