Evidence map›Paper›PMID 32090075›Full record

ArticleBioMed research international2020

Demographic Factors and Cognitive Function Assessments Associated with Mild Cognitive Impairment Progression for the Elderly.

Hong-Yun Qin, Xu-Dong Zhao, Bing-Gen Zhu, Cheng-Ping Hu

Open access · hybridAbstract read
In one paragraph

Article in BioMed research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
2.6field-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

15 citing papers in PubMed, 24 citations in OpenAlex.

  1. Cognition in Treatment-Resistant Depression: Obstacles and Challenges?Advances in experimental medicine and biology · 2026
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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

4 authors at 1 institution in 1 country.

Hong-Yun QinDepartment of Psychiatry, Shanghai Pudong New Area Mental Heath Center, Tongji University School of Medicine, Shanghai 200124, China.
Xu-Dong ZhaoDepartment of Psychiatry, Shanghai Pudong New Area Mental Heath Center, Tongji University School of Medicine, Shanghai 200124, China.
Bing-Gen ZhuDepartment of Psychiatry, Shanghai Pudong New Area Mental Heath Center, Tongji University School of Medicine, Shanghai 200124, China.ORCID https://orcid.org/0000-0002-5174-6932
Cheng-Ping HuDepartment of Psychiatry, Shanghai Pudong New Area Mental Heath Center, Tongji University School of Medicine, Shanghai 200124, China.ORCID https://orcid.org/0000-0002-5120-2273
Tongji University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesIn this study, we aimed to conduct a 6-year follow-up and acquire a large sample dataset to analyze the most important demographic factors and cognitive function scale variables associated with mild cognitive impairment (MCI) progression for an elderly cohort (age ≥ 60 years old).

resultsThe progression outcomes were different between sexes, among age brackets, education degrees, occupations types, and income levels; different progression groups had distinct children numbers (

conclusionsIn conclusion, the MCI progression outcomes were associated with sex, age, education degrees, occupations types, income level, children number, height, and weight. MoCA and MMSE scales are supporting tools to predict the progression outcomes, especially combined with the demographic data.

Indexed as

CognitionDemographyDisease ProgressionNeuropsychological TestsAgedAged, 80 and overCognitive DysfunctionDementiaEducational StatusEmploymentFemaleHumansIncomeMaleRisk FactorsROC Curve

Identifiers

PMID32090075
PMCPMC7031731
OpenAlexW3005406737

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.