Prevalence and determinants of cognitive impairment in older adults with stroke in China: a systematic review
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".