Prevalence and factors influencing cognitive impairment among the older adult stroke survivors: a cross-sectional study
Bibliographic record
Abstract
Background Cognitive impairment as a complication in post-stroke patients has high prevalence throughout the world. However, few studies have focused on the older adult stroke survivors and explored their prevalence and factors of post-stroke cognitive impairment (PSCI). The study aims to evaluate the cognitive status of stroke patients in Hunan Province, China and to determine the potential risk factors associated with PSCI in order to identify the older adult population in advance and promote healthy aging. Methods This cross-sectional study was carried out from August to December, 2021. A total of 520 stroke survivors from 6 tertiary hospitals were randomly selected. The information was collected using the general questionnaire, the Barthel Index Rating Scale and the Mini-mental State Examination (MMSE). Analysis was based on descriptive statistics, chi-square test and the significant variables were included in multivariate logistic regression. The reporting of this cross-sectional study followed the STROBE checklist. Results A total of 195 older adults (40.37%) were screened for cognitive impairment based on the results of the MMSE score. Patients in the PSCI group had a higher proportion of individuals aged 70 or older (35.90% vs. 24.65%, p<0.001). The potential risk factors for post-stroke cognitive impairment in older adults were being aged between 70 and 79 years old (OR = 3.973, 95% CI, 2.346–6.729, p<0.001), being aged 80 years or older (OR = 3.590, 95% CI, 1.373–9.387, p = 0.009), having a low level of education (OR = 9.183, 95% CI, 5.341–15.789, p<0.001), having hypertension (OR = 1.756, 95% CI, 1.121–2.753, p = 0.014), and having a dominant hemisphere lesion (OR = 1.880, 95% CI, 1.193–2.962, p<0.001). Conclusion The prevalence of PSCI was high among Chinese older adults, particularly those aged 80 years or older. The factors identified in our study could assist in the early identification of older adults at risk, develop personalized management plans, and promote healthy aging.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".