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Record W4417182096 · doi:10.3389/fpubh.2025.1706560

Prevalence and determinants of cognitive impairment in older adults with stroke in China: a systematic review

2025· article· en· W4417182096 on OpenAlexaboutno aff
Yang Lan, Chen Xue

Bibliographic record

VenueFrontiers in Public Health · 2025
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsCognitive impairmentStroke (engine)CognitionCohort studyProspective cohort studyCohortDementiaMEDLINECognitive decline

Abstract

fetched live from OpenAlex

Background This systematic review aims to estimate the pooled prevalence of cognitive impairment among older adults Chinese stroke patients and to identify its demographic, clinical, and biochemical determinants, thereby providing evidence to support effective clinical prevention and intervention strategies. Methods Eight databases (CNKI, VIP, WanFang, CBM, PubMed, Web of Science, Embase, and the Cochrane Library) were systematically searched from inception to October 24, 2024. Studies were included if they enrolled Chinese older adults stroke patients (aged ≥60 years), analyzed risk factors for cognitive impairment using case–control/cohort designs, and adopted validated cognitive assessment tools such as the Mini-Mental State Examination [MMSE], Montreal Cognitive Assessment [MoCA]). Data extraction and quality assessment (using the Newcastle–Ottawa Scale) were independently performed by two reviewers. Meta-analysis was performed using Stata version 17.0. A random-effects model was applied for high heterogeneity (I 2 ≥ 50%), whereas a fixed-effects model was used otherwise. Publication bias was assessed using Egger’s test and trim-and-fill method. Results A total of 46 studies, comprising 8,236 older adults stroke patients (3,281 with cognitive impairment) were included in the analysis. The pooled prevalence of cognitive impairment was 42.4% (95%CI: 36.6–48.3%), with significant heterogeneity (I 2 = 97.1%). Subgroup analyses showed higher prevalence in northern China (45.1%) compared with southern China (41.0%) and higher detection rates when using the MoCA (50.5%) than the MMSE (43.4%). The meta-analysis identified 13 robust risk factors, including female gender (OR = 4.167), hypertension (OR = 2.824), diabetes mellitus (OR = 3.344), frontal/temporal lobe infarction (OR = 1.615/1.739), multiple cerebral infarctions (OR = 2.583), brain atrophy (OR = 2.943), hyperhomocysteinemia (OR = 3.043), high-sensitivity C-reactive protein (Hs-CRP) (OR = 4.331), and National Institutes of Health Stroke Scale (NIHSS) scores (OR = 1.977) (all p < 0.05). Publication bias was detected in age-related analyses, and sensitivity analysis confirmed result stability except for CRP. Conclusion Cognitive impairment affects 42.4% of older adults Chinese stroke patients and associated with modifiable risk factors (e.g., hypertension, diabetes) and anatomical correlates (e.g., frontal/temporal infarction). Future research should prioritize large-scale, prospective cohort studies to validate these findings and develop targeted interventions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.025
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0120.010
Bibliometrics0.0100.009
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.319
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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