Evidence map›Paper›PMID 40182143›Full record

ArticleFrontiers in neuroscience2025

Differential cognitive functioning in the digital clock drawing test in AD-MCI and PD-MCI populations.

Chen Wang, Kai Li, Shouqiang Huang, Jiakang Liu, Shuwu Li, Yuting Tu, Bo Wang, Pengpeng Zhang, Yuntian Luo, Tong Chen

Abstract read
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In one paragraph

Article in Frontiers in neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Precision neuropsychology in the area of AI.Frontiers in psychology · 2025
    Article
  3. Article
  4. 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

10 authors.

Chen Wang *School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China.
Kai Li *School of Information Engineering, Hangzhou Medical College, Hangzhou, China.
Shouqiang Huang *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.
Pengpeng ZhangSchool of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China.
Yuntian LuoSchool of Information Engineering, Hangzhou Medical College, 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

Background: Mild cognitive impairment (MCI) is common in Alzheimer's disease (AD) and Parkinson's disease (PD), but there are differences in pathogenesis and cognitive performance between Mild cognitive impairment due to Alzheimer's disease (AD-MCI) and Parkinson's disease with Mild cognitive impairment (PD-MCI) populations. Studies have shown that assessments based on the digital clock drawing test (dCDT) can effectively reflect cognitive deficits. Based on this, we proposed the following research hypothesis: there is a difference in cognitive functioning between AD-MCI and PD-MCI populations in the CDT, and the two populations can be effectively distinguished based on this feature. Methods: To test this hypothesis, we designed the dCDT to extract digital biomarkers that can characterize and quantify cognitive function differences between AD-MCI and PD-MCI populations. We enrolled a total of 40 AD-MCI patients, 40 PD-MCI patients, 41 PD with normal cognition (PD-NC) patients and 40 normal cognition (NC) controls. Results: Through a cross-sectional study, we revealed a difference in cognitive function between AD-MCI and PD-MCI populations in the dCDT, which distinguished AD-MCI from PD-MCI patients, the area under the roc curve (AUC) = 0.923, 95% confidence interval (CI) = 0.866-0.983. The AUC for effective differentiation between AD-MCI and PD-MCI patients with high education (≥12 years of education) was 0.968, CI = 0.927-1.000. By correlation analysis, we found that the overall plotting of task performance score ( Conclusion: The dCDT is a tool that can rapidly and accurately characterize and quantify differences in cognitive functioning in AD-MCI and PD-MCI populations.

Indexed as

Alzheimer’s diseasecognitive functiondigital biomarkersdigital clock drawing testmild cognitive impairmentParkinson’s disease

Identifiers

PMID40182143
PMCPMC11965901

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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.