Evidence map›Paper›PMID 32621406›Full record

ArticleBrain and behavior2020

Diagnostic accuracy of cognitive screening tools under different neuropsychological definitions for poststroke cognitive impairment.

Xiangliang Chen, Yunfei Han, Junshan Zhou, Minmin Ma, Xinfeng Liu

Abstract read
In one paragraph

Article in Brain and behavior, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

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0cells of the map it votes in
6citing 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

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3 · Its place in the literature

Who cites it

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

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4 · The record

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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

5 authors.

Xiangliang ChenDepartment of Neurology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.ORCID 0000-0002-3658-2941
Yunfei HanDepartment of Neurology, Jinling Hospital, Medical School of Nanjing University, Nanjing, China.
Junshan ZhouDepartment of Neurology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
Minmin MaDepartment of Neurology, Jinling Hospital, Medical School of Nanjing University, Nanjing, China.
Xinfeng LiuDepartment of Neurology, Jinling Hospital, Medical School of Nanjing University, Nanjing, China.ORCID 0000-0002-8182-9632

Funding

National Natural Science Foundation of China 81701064
6 · The paper itself

Abstract

objectivesThe accuracy of cognitive screening tools to detect poststroke cognitive impairment (PSCI) was investigated using various neuropsychological definitions.

methodsHospital-based stroke patients underwent a comprehensive neuropsychological assessment. The rate of PSCI was estimated using thresholds of 1, 1.5, or 2 standard deviations below the normal control and memory impairment defined by a single or multiple tests. Meanwhile, the diagnostic accuracy of cognitive screening through face-to-face assessment using the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment Scale (MoCA), and telephone assessment using a 5-minute NINDS-Canadian Stroke Network (NINDS-CSN) scale and a six-item screener (SIS), was both tested under different definitions, with the optimal cutoff selected based on the highest Youden index.

resultsIn stroke patients, the rate of PSCI ranged from 46.3% to 76.3% upon different definitions. The face-to-face MoCA was more consistent with the comprehensive cognitive assessment compared to MMSE. The optimal cutoff of PSCI was MMSE ≤ 27 and MoCA ≤ 19. For the telephone tests, the 5-minute NINDS-CSN assessment was more reliable, and the optimal cutoff was ≤23, while for SIS ≤ 4.

conclusionsCognitive screening tools including the face-to-face MMSE and MoCA, together with the telephone assessment of NINDS-CSN 5-minute protocol and SIS, were simple and effective for detecting PSCI in stroke patients. The corresponding threshold values for PSCI were 27 points, 19 points, 23 points, and 4 points.

Indexed as

Cognitive DysfunctionCanadaCognitionHumansMental Status and Dementia TestsNeuropsychological TestsStrokeneuropsychologypoststroke cognitive impairmentscreening tooltelephone assessment

Identifiers

PMID32621406
PMCPMC7428509

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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.