Evidence map›Paper›PMID 35886713›Full record

ReviewInternational journal of environmental research and public health2022

Bibliometric Analysis of the Knowledge Base and Future Trends on Sarcopenia from 1999-2021.

Yao Xiao, Ziheng Deng, Hangjing Tan, Tiejian Jiang, Zhiheng Chen

Open access · goldAbstract readReview
In one paragraph

Review in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.5field-weighted citation impact, top 10% 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

8 citing papers in PubMed, 21 citations in OpenAlex.

  1. Review
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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.

Yao XiaoDepartment of Endocrinology, Endocrinology Research Center, Xiangya Hospital of Central South University, Changsha 410008, China.
Ziheng DengInstitute of Reproduction and Stem Cell Engineering, School of Basic Medical Science, Central South University, Changsha 410008, China.
Hangjing TanInstitute of Reproduction and Stem Cell Engineering, School of Basic Medical Science, Central South University, Changsha 410008, China.
Tiejian JiangDepartment of Endocrinology, Endocrinology Research Center, Xiangya Hospital of Central South University, Changsha 410008, China.
Zhiheng ChenDepartment of Pediatrics, The Third Xiangya Hospital, Central South University, No. 138 Tongzipo Road, Yuelu District, Changsha 410013, China.
Central South University · CNXiangya Hospital Central South University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sarcopenia is characterized by progressive loss of muscle mass and function, and it is becoming a serious public health problem with the aging population. However, a comprehensive overview of the knowledge base and future trends is still lacking. The articles and reviews with "sarcopenia" in their title published from 1999 to 2021 in the SCIE database were retrieved. We used Microsoft Excel, VOSviewer, and CiteSpace to conduct a descriptive and bibliometric analysis. A total of 3582 publications were collected, from 4 published in 2000 increasing dramatically to 850 documents in 2021. The USA was the most productive country, with the most citations. The Catholic University of the Sacred Heart and Landi F were the most influential organization and author in this field, respectively. The core journal in this field was the

Indexed as

BibliometricsPublicationsEfficiencyForecastingKnowledge Basesbibliometric analysisCiteSpacesarcopeniaVOSviewer

Identifiers

PMID35886713
PMCPMC9320125
OpenAlexW4286489453

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.