Domain‐specific cognitive trajectory among patients with minor stroke or transient ischemic attack in a 6‐year Asian cohort: Temporal patterns and indicators
Bibliographic record
Abstract
Abstract Background This study aimed to assess the 6‐year domain‐specific patterns of cognitive trajectory among survivors of minor stroke or transient ischemic attack (TIA) in a multi‐ethnic clinical cohort in Singapore, and to identify possible demographic, clinical and vascular indicators of different trajectory patterns. Method Elderly participants completed cognitive and clinical assessments at 2 weeks, 3‐6 months, 1 year, 3/4 years, 5 years and 6 years post‐stroke. Global and domain‐specific cognition was transformed to standardized z‐scores. Generalized estimating equation and mixed linear model were used to examine baseline predictors of long‐term cognitive impairment and domain‐specific cognitive trajectory. Survival analysis was performed for incident cognitive impairment during the 6‐year study duration. Result A total of 244 participants were included in the analysis. A fluctuating yet stable pattern was observed in all cognitive domains, except for visual memory, which showed a significant improvement across the six‐year follow‐up. Regardless of baseline characteristics, baseline MoCA was associated with lower odds of long‐term cognitive impairment (0.67, 95%CI= (0.60, 0.75)), whilst both MoCA improvement from baseline to 3‐6 months, and from baseline to 1 year were associated with lower risk of cognitive impairment (OR 0.80, 95%CI= (0.71, 0.90) and 0.86, 95%CI= (0.77, 0.97) respectively) after controlling for covariates. Conclusion Post‐stroke cognitive trajectory showed varying patterns across different cognitive domains. Our findings provide evidence on practice effects of the MoCA which can be used for identifying patients with higher risk of long‐term cognitive impairment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".