Temporal Evolution of Poststroke Cognitive Impairment Using the Montreal Cognitive Assessment
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The Montreal Cognitive Assessment (MoCA) is nowadays recommended for the screening of poststroke cognitive impairment. However, little is known about the temporal evolution of MoCA-assessed cognition after stroke. The objective of this study was to examine the temporal pattern of overall and domain-specific cognition at 2 and 6 months after stroke using the MoCA and to identify patient groups at risk for cognitive impairment at 6 months after stroke. METHODS: Prospective cohort study in which 324 patients were administered the MoCA at 2 and 6 months post stroke. Cognitive impairment was defined as MoCA<26. Differences in cognitive impairment rates between 2 and 6 months post stroke were analyzed in different subgroups. Patients with MoCA score <26 at 2 months, who improved by ≥2 points by 6 months, were defined as reverters. Logistic regression analyses were used to identify determinants of (1) cognitive impairment at 6 months post stroke and (2) reverter status. RESULTS: Between 2 and 6 months post stroke, mean MoCA score improved from 23.7 (3.9) to 24.7 (3.5), P<0.001. Prevalence of cognitive impairment at 2 months was 66.4%, compared with 51.9% at 6 months (P<0.001). More comorbidity and presence of cognitive impairment at 2 months were significant independent predictors of cognitive impairment 6 months post stroke. No significant determinants of reverter status were identified. CONCLUSIONS: Although cognitive improvement is seen ≤6 months post stroke, long-term cognitive deficits are prevalent. Identifying patients at risk of cognitive impairment is, therefore, important as well as targeting interventions to this group.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".