Evidence map›Paper›PMID 38773403›Full record

ArticleBMC geriatrics2024

Sarcopenic obesity in nursing home residents: a multi-center study on diagnostic methods and their association with instrumental activities of daily living.

Huiyu Tang, Runjie Li, Ruicen Li, Rongna Lian, Xiaoyan Chen, Wenhua Jiang, Jiaojiao Jiang, Ming Yang

Abstract read
In one paragraph

Article in BMC geriatrics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

8 authors.

Huiyu Tang *Center of Gerontology and Geriatrics, West China Hospital, Sichuan University, Chengdu, China.
Runjie Li *Center of Gerontology and Geriatrics, West China Hospital, Sichuan University, Chengdu, China.
Ruicen LiHealth Management Center, West China Hospital, Sichuan University, Chengdu, China.
Rongna LianCenter of Gerontology and Geriatrics, West China Hospital, Sichuan University, Chengdu, China.
Xiaoyan ChenCenter of Gerontology and Geriatrics, West China Hospital, Sichuan University, Chengdu, China.
Wenhua JiangCenter of Gerontology and Geriatrics, West China Hospital, Sichuan University, Chengdu, China.
Jiaojiao JiangRehabilitation Center, West China Hospital, Sichuan University, Chengdu, China. jiangjiaojiao1997@163.com.
Ming YangCenter of Gerontology and Geriatrics, West China Hospital, Sichuan University, Chengdu, China. yangmier@gmail.com.

Funding

Health and Family Planning Commission of Sichuan Province No. ZH2023-103Sichuan Science and Technology Program 2022YFS0130
6 · The paper itself

Abstract

backgroundSarcopenic obesity (SO) in nursing home residents is rarely studied. We aimed to evaluate and compare the prevalence and consistency of different SO diagnostic methods and to investigate which criterion demonstrated a stronger association with instrumental activities of daily living (IADL) disability.

methodsWe consecutively recruited older adults aged ≥ 60 years, residing in 15 nursing homes in Zigong City, China. Sarcopenia obesity was defined according to the European Society for Clinical Nutrition and Metabolism (ESPEN) and the European Association for the Study of Obesity criteria (SO

resultsWe included 832 participants (median age 73.0 years, 296 women). The prevalence of SO

conclusionsBoth SO

Indexed as

Activities of Daily LivingNursing HomesObesitySarcopeniaAgedAged, 80 and overBody Mass IndexChinaCross-Sectional StudiesFemaleGeriatric AssessmentHumansMaleMiddle AgedPrevalenceFunctional assessmentLong-term careMuscle qualityObese sarcopeniaPhysical function

Identifiers

PMID38773403
PMCPMC11110436

What OpenQuestion holds

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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.