Evidence map›Paper›PMID 30787413›Full record

ArticleScientific reports2019

Skeletal Muscle Mass as a Mortality Predictor among Nonagenarians and Centenarians: A Prospective Cohort Study.

Hui Wang, Shan Hai, Yixin Liu, Ying Liu, Birong Dong

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed, 5 pooled it
4.3field-weighted citation impact, top 5% of its field
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

40 citing papers in PubMed, 5 syntheses or guidelines pooled it, 68 citations in OpenAlex.

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  10. Machine learning prediction of survival in centenarians after age 100: a retrospective, population-based cohort study.The journals of gerontology. Series A, Biological sciences and medical sciences · 2025
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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

5 authors at 2 institutions in 1 country.

Hui WangDepartment of Geriatrics and National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China.
Shan HaiCenter of Gerontology and Geriatrics, West China Hospital of Sichuan University, Chengdu, China.
Yixin LiuCenter of Gerontology and Geriatrics, West China Hospital of Sichuan University, Chengdu, China.
Ying LiuCenter of Gerontology and Geriatrics, West China Hospital of Sichuan University, Chengdu, China.
Birong DongDepartment of Geriatrics and National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China. birongdong@163.com.
Sichuan University · CNWest China Hospital of Sichuan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aimed to evaluate the association between skeletal muscle mass and long-term all-cause mortality among nonagenarians and centenarians in China. We used data from the Project of Longevity and Aging in Dujiangyan (PLAD). A total of 738 community-dwelling people aged ≥ 90 years (mean age of 93.5 ± 3.2 years) were analyzed in this study. The appendicular skeletal muscle mass (ASM) was estimated using a previously validated anthropometric equation. The information on the survival status was requested from the local government registries during the 4 year follow-up period following the baseline investigation. The mean muscle mass index (SMI) was 6.11 ± 0.53 kg/m

Indexed as

Activities of Daily LivingAgedAged, 80 and overAgingBiomassBody CompositionChinaFemaleGeriatric AssessmentHumansIndependent LivingMaleMuscle, SkeletalMuscular DiseasesPersons with DisabilitiesProportional Hazards Models

Identifiers

PMID30787413
PMCPMC6382937
OpenAlexW2917974390

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.