Prevalence of cognitive impairment in acute ischaemic stroke and use of Alberta Stroke Programme Early CT Score (ASPECTS) for early prediction of post-stroke cognitive impairment
Bibliographic record
Abstract
AIM OF THE STUDY: This study aims to assess the prevalence of post-stroke cognitive impairment, and to evaluate the correlation of ASPECTS with impaired cognition. MATERIALS AND METHODS: 150 patients presenting with acute middle cerebral artery territory ischaemic stroke were included in this study. Risk factors of ischaemic stroke and the initial NIHSS were determined. An initial and a follow-up non-contrast CT brain were carried out after seven days which were assessed by ASPECTS. The prevalence of cognitive impairment was determined by MoCA during the follow up of patients after three months. Correlations of ASPECTS, NIHSS and MoCA were done by Spearman correlation. Multivariate logistic regression analysis was carried out for the independent variables of cognitive impairment. RESULTS: The prevalence of post-stroke cognitive impairment in this study, according to the threshold for cognitive impairment with a MoCA score of 25 or less, was 25.3% (38 patients). Significant positive correlations between ASPECTS and total MoCA test domains were found (r = 0.73 and P = 0.002). Logistic regression analysis demonstrated that the independent factors associated with cognitive impairment were older age, certain domains of the MoCA test like executive functions, memory, attention, language, NIHSS, HTN, and ASPECTS. CONCLUSIONS AND CLINICAL IMPLICATIONS: There is a prevalence of cognitive impairment in about 25% of patients after three months of follow-up in cases with acute ischaemic stroke. ASPECTS is directly correlated with cognitive impairment, and may be considered as a biomarker of post-stroke cognitive impairment.
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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 | 0.003 |
| 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".