Evidence map›Paper›PMID 41867027›Full record

ArticleAlzheimer's & dementia : the journal of the Alzheimer's Association2026

Classifying cognitive status with TabCAT Match and UDS executive function tests.

Alexandra J Weigand, Elena Tsoy, Kelly J Atkins, Sabrina Jarrott, Sarah Tomaszewski Farias, Aaron W Scheffler, Tiffany Brailow, Katherine P Rankin, Joel H Kramer, Katherine L Possin

Abstract read
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In one paragraph

Article in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2026. 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
–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

0 citing papers in PubMed.

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

10 authors.

Alexandra J WeigandDepartment of Neurology, Edward and Pearl Fein Memory and Aging Center, University of California San Francisco, San Francisco, California, USA.
Elena TsoyDepartment of Neurology, Edward and Pearl Fein Memory and Aging Center, University of California San Francisco, San Francisco, California, USA.
Kelly J AtkinsDepartment of Neurology, Edward and Pearl Fein Memory and Aging Center, University of California San Francisco, San Francisco, California, USA.
Sabrina JarrottDepartment of Neurology, Edward and Pearl Fein Memory and Aging Center, University of California San Francisco, San Francisco, California, USA.
Sarah Tomaszewski FariasDepartment of Neurology, University of California Davis, Sacramento, California, USA.
Aaron W SchefflerDepartment of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, USA.
Tiffany BrailowDepartment of Neurology, Edward and Pearl Fein Memory and Aging Center, University of California San Francisco, San Francisco, California, USA.
Katherine P RankinDepartment of Neurology, Edward and Pearl Fein Memory and Aging Center, University of California San Francisco, San Francisco, California, USA.
Joel H KramerDepartment of Neurology, Edward and Pearl Fein Memory and Aging Center, University of California San Francisco, San Francisco, California, USA.
Katherine L PossinDepartment of Neurology, Edward and Pearl Fein Memory and Aging Center, University of California San Francisco, San Francisco, California, USA.ORCID 0000-0002-6623-3793

Funding

Research Education ComponentP30AG062422 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Katherine P Rankin · 2019 to 2026
$36.9M
UC Davis Alzheimer's Disease Research CenterP30AG072972 · NIA · UNIVERSITY OF CALIFORNIA AT DAVIS · PI Charles DeCarli, Rachel A Whitmer · 2021 to 2026
$25.2M
Augmenting Nurse Support and EHR Integration for the Pragmatic Trial of the UCSF-BHAU01NS128913 · NINDS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Katherine Laurel Possin · 2022 to 2026
$8.2M
Reimagining Precision Medicine Approaches to AD DiagnosisR35AG072362 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI POSSIN, KATHERINE LAUREL, RABINOVICI, GIL DAN · 2021 to 2025
$4.8M
NIA NIH HHS P30 AG062422NIA NIH HHS P30 AG072972NIA NIH HHS R35 AG072362NINDS NIH HHS U01 NS128913
6 · The paper itself

Abstract

introductionTablet-based Cognitive Assessment Tool (TabCAT) Match, a digital neuropsychological test, was compared to measures of executive function from the National Alzheimer's Coordinating Center's Uniform Dataset (UDS).

methodsTabCAT Match data were available for 1792 clinically diverse older adults. Receiver operating characteristic curves and analyses of covariance examined classification of Clinical Dementia Rating Scale (CDR) stages based on Match and UDS executive function scores. Sensitivity analyses were conducted across education level and testing language groups.

resultsAll measures had utility in differentiating CDR stages, with Match displaying the highest classification accuracy (AUC = 0.862) for cognitively unimpaired (CDR 0, clinically normal) versus cognitively impaired (CDR ≥ 0.5, mild cognitive impairment/dementia). The advantages of Match were especially apparent in non-English tested groups relative to UDS tests. DISCUSSION: The identification of efficient neuropsychological measures, such as TabCAT Match, that are easily administered, sensitive to impairment, and appropriate across demographic groups is critical for optimizing diagnosis and clinical monitoring.

Indexed as

Cognitive DysfunctionExecutive FunctionNeuropsychological TestsAgedAged, 80 and overComputers, HandheldFemaleHumansMaleROC Curveagingdementiadigital cognitive testsexecutive functionneuropsychologyteleneuropsychology

Identifiers

PMID41867027
PMCPMC13093779

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LicenceCC BY-NC
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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.