Frequency of Cognitive Impairment Among Malaysian Elderly Patients Following First Ischaemic Stroke—A Case Control Study
Bibliographic record
Abstract
Background: Stroke is highly prevalent globally and is an important cause of cognitive impairment and dementia. Aims: We determined the frequency of post-stroke cognitive impairment (PSCI) at 1, 3 and 6 months among patients with first clinical ischemic stroke compared to risk and age-matched controls. Methods: This study involved 32 cases and 32 controls, and was conducted over 6 months. Cases were inpatients aged >60 with first clinical ischemic stroke. Controls were age-matched subjects without prior stroke. Montreal Cognitive Assessment (MoCA) was performed in all patients at 1 month, 3 month and 6 month post stroke. A MoCA score of < 26 was used for mild PSCI and <22 for moderate PSCI (post stroke dementia). Results: Post-stroke dementia was detected in 12 patients (37.5%) at 1st month, in 13 (40.6%) at 3rd month and 15 (48.4%) at 6th months. Mild PSCI was present in 7 patients (21.6%) at 1 month, 16 patients (50%) at 3 months and 15 patients (48%) at 6 months. The odds ratio (OR) for post-stroke dementia was 3.2 (95%CI 0.98-10.68; p=0.05) at 1 month; 3.69(95% CI 1.13-12.11; p=0.031) at 3 months, and 4.88 (95% CI 1.49-15.99; p=0.009) at 6 months. Years of education was an independent predictor for dementia (OR 0.60; p=0.046). The OR for post-stroke dementia at 6th month was 7.23 with education level adjusted (95%CI 1.46-35.86, p=0.015). Conclusion: The frequency of PSCI was high as early as 1 month after stroke. Stroke alone conferred a 7.2 times risk for post-stroke dementia compared to controls.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".