Evidence map›Paper›PMID 39569363›Full record

ArticleiScience2024

Development and validation of a screening tool for sarcopenia in Chinese nursing home residents.

Kaili Sun, Xucheng Wu, Jiening Yu, Weiran Chen, Xueqing Jia, Dongwen Li, Hao Lyu, Wenhai Huang, Hongqing Tian, Weijing Gao and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2024. 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

16 authors.

Kaili SunThe Second Affiliated Hospital, School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310058, China.
Xucheng WuThe Second Affiliated Hospital, School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310058, China.
Jiening YuThe Second Affiliated Hospital, School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310058, China.
Weiran ChenThe Second Affiliated Hospital, School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310058, China.
Xueqing JiaThe Second Affiliated Hospital, School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310058, China.
Dongwen LiSchool of Public Affairs, Zhejiang University, Hangzhou 310058, Zhejiang, China.
Hao LyuSchool of Public Affairs, Zhejiang University, Hangzhou 310058, Zhejiang, China.
Wenhai HuangShenzhen Yolanda Technology Co, Ltd, Shenzhen 518000, China.
Hongqing TianShenzhen Yolanda Technology Co, Ltd, Shenzhen 518000, China.
Weijing GaoThe Second Affiliated Hospital, School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310058, China.
Bin ZhuThe Second Affiliated Hospital, School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310058, China.
Xinwei LyuInstitute of Epidemiology and Health Care, University College London, London, UK.
Li WangInstitute of Nursing Research, and Department of Nursing of the Fourth Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang, China.
Jing ShaoInstitute of Nursing Research, and Department of Nursing of the Fourth Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang, China.
Xiaoting LiuSchool of Public Affairs, Zhejiang University, Hangzhou 310058, Zhejiang, China.
Zuyun LiuThe Second Affiliated Hospital, School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310058, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sarcopenia, characterized by the loss of muscle mass and function, is a critical health issue in nursing home residents. Given the high prevalence of sarcopenia in care settings, effective screening is crucial. This study compared the diagnostic accuracy of two conventional sarcopenia screening tools, SARC-F and SARC-CalF, and developed an alternative tool, SEAC, specifically for nursing home residents. In a sample of 679 older adults from Zhejiang Province, SARC-CalF exhibited better sensitivity and diagnostic accuracy compared to SARC-F. The SEAC tool, incorporating strength, exhaustion, assistance in walking, and calf circumference, demonstrated the highest diagnostic accuracy and sensitivity among the three screening tools. These findings suggest that SEAC could serve as a valuable tool for large-scale sarcopenia screening in nursing homes, potentially improving early detection and management of sarcopenia in this vulnerable population.

Indexed as

Diagnostic procedureMeasuring systemPublic health

Identifiers

PMID39569363
PMCPMC11577178

What OpenQuestion holds

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Registered trials

None linked

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.