Cognitive change following stroke and its impact on long-term care
Bibliographic record
Abstract
Aims/Background: The outcomes of cognitive assessments after stroke provide important information for the implementation of immediate specialist interventions. This study investigated the trajectory of cognitive changes in an acute stroke sample. This was done by examining the relationship between cognitive changes following stroke as determined by the Montreal Cognitive Assessment (MoCA) and patients' discharge locations (own home, home with social care package, and placement). Method: The data of 124 hospitalised patients with acute stroke was retrospectively analysed. Patients' age ranged from 51 to 96 years, 77 patients were women and 47 were men. Data of post-stroke cognitive outcomes as represented by the MoCA results and discharge location were collected. Results: Patients' MoCA Mean outcome was 12.12 and Standard Deviation 7.77. Significant differences were found between patients with low post-stroke cognitive functioning, requiring more intensive care after discharge (i.e. placement), and those with better cognition, who subsequently were discharged home. The MoCA subdomain ‘Concentration and Calculation’ was associated most with discharge location. The study outcomes can potentially improve the efficiency of very early interventions and hospital discharge plans for acute stroke patients.
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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 | 0.010 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".