Evidence map›Paper›PMID 39840014›Full record

ArticleFrontiers in neuroscience2024

A study on the exploration of mild cognitive impairment in Parkinson's disease based on decision-making cognitive computing.

Shouqiang Huang, Kai Li, Chen Wang, Jiakang Liu, Shuwu Li, Yuting Tu, Bo Wang, Huangqin Feng, Qin Yu, Hongzhou Lin and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

14 authors.

Shouqiang Huang *School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China.
Kai Li *School of Information Engineering, Hangzhou Medical College, Hangzhou, China.
Chen Wang *School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China.
Jiakang LiuSchool of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China.
Shuwu LiSchool of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China.
Yuting TuSchool of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China.
Bo WangSchool of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China.
Huangqin FengSchool of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China.
Qin YuSchool of Information Engineering, Hangzhou Medical College, Hangzhou, China.
Hongzhou LinSchool of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China.
Yuzhe XuSchool of Information Engineering, Hangzhou Medical College, Hangzhou, China.
Jinghang WuSchool of Information Engineering, Hangzhou Medical College, Hangzhou, China.
Ting ZhangSchool of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China.
Tong ChenDepartment of Neurology, The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mild cognitive impairment in Parkinson's disease (PD-MCI) as an independent risk factor for dementia in Parkinson's disease has prognostic value in predicting dementia in PD patients. It was found that the calculation of cognitive function decision-making could better evaluate the cognitive function of PD-MCI. Therefore, this study explored deficits in decision-making cognitive function in PD-MCI population, and mined novel digital biomarkers for recognizing early cognitive decline in PD-MCI through an independently designed maze decision-making digital assessment paradigm. This study included 30 healthy controls 37 PD with normal cognition (PD-NC) and 40 PD-MCI patients. Through difference comparison and stepwise regression analysis, two digital decision-making biomarkers, total decision time and performance average acceleration, were screened, and their joint area under curve for the ability to discriminate between PD-MCI and PD-NC was 0.909, and for the ability to discriminate between PD-MCI and NC was 0.942. In addition, it was found that maze digital decision-making biomarkers had greater early warning efficacy in men than in women. Unlike traditional methods, this study used digital dynamic assessment to reveal possible decision-making cognitive deficits in the PD-MCI populations, which provides new ideas for effective screening for PD-MCI.

Indexed as

decision-makingdigital biomarkersexplorationmild cognitive impairmentParkinson’s disease

Identifiers

PMID39840014
PMCPMC11747548

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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.