Evidence map›Paper›PMID 35284388›Full record

ArticleFrontiers in public health2022

Development and Validation of a New Simple Functional Score in the Older Chinese Population.

Xingqi Cao, Chen Chen, Liu He, Zhoutao Zheng, Jingyun Zhang, Emiel O Hoogendijk, Xiaoting Liu, Shujuan Li, Xiaofeng Wang, Yimin Zhu and 1 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.1field-weighted citation impact, top 56% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

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

11 authors at 5 institutions in 2 countries.

Xingqi CaoCenter for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China.
Chen ChenNational Center for Acquired Immunodeficiency Syndrome/Sexually Transmitted Disease (AIDS/STD) Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Liu HeCenter for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China.
Zhoutao ZhengCenter for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China.
Jingyun ZhangCenter for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China.
Emiel O HoogendijkDepartment of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC-Location VU University Medical Center, Amsterdam, Netherlands.
Xiaoting LiuSchool of Public Affairs, Zhejiang University, Hangzhou, China.
Shujuan LiDepartment of Neurology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
Xiaofeng WangNational Clinical Research Center for Aging and Medicine, Huashan Hospital, and Human Phenome Institute, Fudan University, Shanghai, China.
Yimin ZhuDepartment of Epidemiology and Biostatistics, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China.
Zuyun LiuCenter for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China.
Second Affiliated Hospital of Zhejiang University · CNZhejiang University · CNAmsterdam UMC Location VUmc · NLCapital Medical University · CNFudan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Existing aging metrics incorporating cognitive and physical function are often not feasible for application in research and clinical practice. Therefore, this study aimed to develop and validate a new simple functional score based on self-reported cognitive and physical function in the older Chinese population. Methods: The development sample included 3,929 older adults aged 60-95 years from the China Health and Retirement Longitudinal Study (CHARLS). The validation sample included 1,345 older adults aged 60-87 years from the Rugao Longitudinal Aging study (RLAS). Logistic regression models and receiver operating characteristic curves were used to examine the associations of the new functional score with all-cause mortality risk. Results: Six items were selected to construct the new functional score in CHARLS. This functional score was associated with all-cause mortality risk, with an adjusted odds ratio of 1.10 (95% confidence interval = 1.07, 1.13). This functional score presented additional predictive utility beyond age and sex, as demonstrated by the significantly increased C-statistic, integrated discrimination improvement (IDI), and continuous net reclassification improvement (NRI) (all Conclusions: A new functional score based on six self-reported items was developed and validated in the older Chinese population, and was demonstrated to be a simple and practical tool to assess functional deterioration, showing good feasibility, and performance.

Indexed as

AgingAsian PeopleAgedChinaHumansLongitudinal StudiesROC CurveagingChinesecognitive functionmortalityphysical function

Identifiers

PMID35284388
PMCPMC8907530
OpenAlexW4213434255

What OpenQuestion holds

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LicenceCC BY
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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.