Post-Stroke Cognitive Impairment: A Narrative Review of the Comprehensive Screening and Detecting Process
Bibliographic record
Abstract
Purpose To examine screening procedures and tools for post-stroke cognitive impairment (PSCI) to guide future practices and research. Method Searches in PubMed, CINAHL, PsycINFO, and Google Scholar included articles from 2013 to 2023 focusing on individuals with first ever ischemic stroke and confirming PSCI within 1 year. Thematic analysis was synthesized narratively. Results Eight studies (two cross-sectional and six prospective cohorts) with 25,443 participants were reviewed. Screening for PSCI was typically performed within 3 to 6 months post-stroke. Montreal Cognitive Assessment and Mini-Mental State Examination were the most commonly used tools, but cutoff scores varied widely. Screening involved pre- and post-stroke cognitive screening and identifying risk factors. Conclusion Significant variability exists in PSCI assessment tools, cutoff, and timing. Further research is needed to standardize screening protocols, focusing on criteria, timing, accuracy, and feasibility. Early and repeated screening with risk management can improve PSCI prevention. [ Journal of Gerontological Nursing, 51 (3), 19–27.]
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".