Evidence map›Paper›PMID 30705371›Full record

ArticleScientific reports2019

Integrated Cognitive Assessment: Speed and Accuracy of Visual Processing as a Reliable Proxy to Cognitive Performance.

Seyed-Mahdi Khaligh-Razavi, Sina Habibi, Maryam Sadeghi, Haniye Marefat, Mahdiyeh Khanbagi, Seyed Massood Nabavi, Elham Sadeghi, Chris Kalafatis

Open access · goldAbstract readComparative Study
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
3.1field-weighted citation impact, top 8% 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

20 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.

  1. Current Update on the Clinical Utility of MMSE and MoCA for Stroke Patients in Asia: A Systematic Review.International journal of environmental research and public health · 2021
    Pooled it
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  7. Cognitive ergonomics and robotic surgery.Journal of robotic surgery · 2024
    Review
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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

8 authors at 5 institutions in 3 countries.

Seyed-Mahdi Khaligh-RazaviComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. skhaligh@mit.edu.ORCID 0000-0002-5700-1704
Sina HabibiCognetivity ltd, London, UK.
Maryam SadeghiUniversity of Tehran, Tehran, Iran.
Haniye MarefatSchool of Cognitive Sciences (SCS), Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
Mahdiyeh KhanbagiDepartment of Brain and Cognitive Sciences, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran.
Seyed Massood NabaviDepartment of Brain and Cognitive Sciences, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran.
Elham SadeghiUniversity of Tehran, Tehran, Iran.
Chris KalafatisCognetivity ltd, London, UK.
Royan Institute · IRUniversity of Tehran · IRInstitute for Research in Fundamental Sciences · IRnLIGHT (United States) · USSouth London and Maudsley NHS Foundation Trust · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Various mental disorders are accompanied by some degree of cognitive impairment. Particularly in neurodegenerative disorders, cognitive impairment is the phenotypical hallmark of the disease. Effective, accurate and timely cognitive assessment is key to early diagnosis of this family of mental disorders. Current standard-of-care techniques for cognitive assessment are primarily paper-based, and need to be administered by a healthcare professional; they are additionally language and education-dependent and typically suffer from a learning bias. These tests are thus not ideal for large-scale pro-active cognitive screening and disease progression monitoring. We developed the Integrated Cognitive Assessment (referred to as CGN_ICA), a 5-minute computerized cognitive assessment tool based on a rapid visual categorization task, in which a series of carefully selected natural images of varied difficulty are presented to participants. Overall 448 participants, across a wide age-range with different levels of education took the CGN_ICA test. We compared participants' CGN_ICA test results with a variety of standard pen-and-paper tests, such as Symbol Digit Modalities Test (SDMT) and Montreal Cognitive Assessment (MoCA), that are routinely used to assess cognitive performance. CGN_ICA had excellent test-retest reliability, showed convergent validity with the standard-of-care cognitive tests used here, and demonstrated to be suitable for micro-monitoring of cognitive performance.

Indexed as

CognitionCognition DisordersMental Status and Dementia TestsVisual PerceptionAdultAgedAged, 80 and overFemaleHumansMaleMiddle Aged

Identifiers

PMID30705371
PMCPMC6355897
OpenAlexW2949263718

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.