Evidence map›Paper›PMID 37654085›Full record

ArticleAlzheimer's & dementia : the journal of the Alzheimer's Association2024

Toward digitally screening and profiling AD: A GAMLSS approach of MemTrax in China.

Wanwan Liu, Ling Yu, Qiuqiong Deng, Yunrong Li, Peiwen Lu, Jie Yang, Fei Chen, Feng Li, Xianbo Zhou, Michael F Bergeron and 2 more

Abstract read
In one paragraph

Article in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
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  9. Toward digitally screening and profiling AD: A GAMLSS approach of MemTrax in China.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024
    Article
  10. Development of amyloid-negative neuropsychological norms using GAMLSS.Alzheimer's & dementia (Amsterdam, Netherlands)
    Article
  11. 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

12 authors.

Wanwan LiuHealth Management Center, Renji Hospital of Medical School of Shanghai Jiaotong University, Shanghai, China.
Ling YuHealth Management Center, Renji Hospital of Medical School of Shanghai Jiaotong University, Shanghai, China.
Qiuqiong DengHealth Management Center, Renji Hospital of Medical School of Shanghai Jiaotong University, Shanghai, China.
Yunrong LiHealth Management Center, Renji Hospital of Medical School of Shanghai Jiaotong University, Shanghai, China.
Peiwen LuDepartment of Neurology, Renji Hospital of Medical School of Shanghai Jiaotong University, Shanghai, China.
Jie YangDepartment of Neurology, Renji Hospital of Medical School of Shanghai Jiaotong University, Shanghai, China.
Fei ChenHealth Management Center, Renji Hospital of Medical School of Shanghai Jiaotong University, Shanghai, China.
Feng LiKunming Escher Technology Co. Ltd, Kunming, Yunnan, China.
Xianbo ZhouCenter for Alzheimer's Research, Washington Institute of Clinical Research, Vienna, Virginia, USA.
Michael F BergeronVisiting Scholar, Department of Health Sciences, University of Hartford, West Hartford, Connecticut, USA.
John Wesson AshfordWar Related Illness and Injury Study Center, VA Palo Alto HCS, Palo Alto, California, USA.
Qun XuHealth Management Center, Renji Hospital of Medical School of Shanghai Jiaotong University, Shanghai, China.

Funding

Natural Science Foundation of Shanghai 23ZR1439200
6 · The paper itself

Abstract

purposesTo establish a normative range of MemTrax (MTx) metrics in the Chinese population.

methodsThe correct response percentage (MTx-%C) and mean response time (MTx-RT) were obtained and the composite scores (MTx-Cp) calculated. Generalized additive models for location, shape and scale (GAMLSS) were applied to create percentile curves and evaluate goodness of fit, and the speed-accuracy trade-off was investigated.

results26,633 subjects, including 13,771 (51.71%) men participated in this study. Age- and education-specific percentiles of the metrics were generated. Q tests and worm plots indicated adequate fit for models of MTx-RT and MTx-Cp. Models of MTx-%C for the low and intermediate education fit acceptably, but not well enough for a high level of education. A significant speed-accuracy trade-off was observed for MTx-%C from 72 to 94.

conclusionsGAMLSS is a reliable method to generate smoothed age- and education-specific percentile curves of MTx metrics, which may be adopted for mass screening and follow-ups addressing Alzheimer's disease or other cognitive diseases. HIGHLIGHTS: GAMLSS was applied to establish nonlinear percentile curves of cognitive decline. Subjects with a high level of education demonstrate a later onset and slower decline of cognition. Speed-accuracy trade-off effects were observed in a subgroup with moderate accuracy. MemTrax can be used as a mass-screen instrument for active cognition health management advice.

Indexed as

Alzheimer DiseaseCognition DisordersCognitive DysfunctionCognitionEducational StatusFemaleHumansMaleAlzheimer's diseasecognitive screeningdigital biomarkersGAMLSSnorms

Identifiers

PMID37654085
PMCPMC10916970

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.