Prevalence and Determinants of Postischemic Stroke Cognitive Impairment in Older Persons
Bibliographic record
Abstract
Abstract Background: It is essential to identify the burden of poststroke cognitive impairment (PSCI) and to frame strategies for its prevention and progression in a developing country. Aim: The aim of the study was to estimate the prevalence and identify the risk factors of PSCI in older persons who survived an ischemic stroke. Materials and Methods: Patients with acute stroke satisfying the inclusion criteria were recruited. Clinical and demographic data, baseline functional capacity, and cognition as assessed by the Barthel index and the Informant Questionnaire on Cognitive Decline in the Elderly, respectively, were collected. The patients were then administered the Confusion Assessment Method, Mini-Cog, Montreal Cognitive Assessment (MoCA), and Frontal Assessment Battery within the 1 st week of stroke and were reassessed at 1 month. The diagnosis of PSCI was done based on MoCA score within 1 week and at 1 month after stroke, and the analysis of risk factors for PSCI was done based on MoCA score at 1 month. Results: The prevalence of PSCI in this study was 63.8% within the 1 st week and 71.8% at 1 month after the stroke. Lower educational and occupational status, higher Charlson Comorbidity Index, presence of delirium during the 1 st week after stroke, poststroke depression, higher National Institutes of Health Stroke Scale score, and higher Modified Rankin Scale score were found to be risk factors for the development of PSCI on univariate analysis. Lower socioeconomic status was found to be a risk factor on both univariate and multivariate analyses. Conclusion: The prevalence of PSCI was 71.8% at 1 month after stroke. Lower socioeconomic status was found to be a risk factor for PSCI. Larger studies are needed to identify various modifiable risk factors, to improve the quality of life in older stroke survivors.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".