Domain-Specific Cognitive Trajectories Among Patients with Minor Stroke or Transient Ischemic Attack in a 6-Year Prospective Asian Cohort: Serial Patterns and Indicators
Bibliographic record
Abstract
BACKGROUND: Long-term post-stroke cognitive impairment (PSCI) has often been overlooked, especially among patients with minor stroke or transient ischemic attack (TIA). OBJECTIVE: To assess 6-year domain-specific cognitive trajectories among survivors of minor stroke or TIA and to identify possible indicators associated with cognitive trajectories, as well as long-term and incident PSCI. METHODS: Eligible participants completed cognitive and clinical assessments at baseline (2 weeks after stroke) and up to 5 follow-up visits in 6 years. Mixed linear models and generalized estimating equations were adopted to analyze longitudinal data and survival analysis to explore incident PSCI, controlling for demographic, clinical, and vascular indicators. RESULTS: The prevalence of PSCI and mortality rate ranged from 34.6% to 53.7%, and 0 to 7.7% respectively, among 244 patients. Incidence of PSCI was 21.9%. While visual memory demonstrated a significant improvement (p < 0.05), other cognitive domains showed a fluctuating yet stable pattern across visits (all ps > 0.05). Besides age, baseline IQCODE (attention: -0.218 SD/y, executive function: -0.238 SD/y, visual memory: -0.266 SD/y), and MoCA improvement within 1 year (visuoconstruction: 0.007 SD/y, verbal memory: 0.012 SD/y) were associated with longitudinal cognitive changes. Baseline MoCA (OR = 0.66, 95% CI = [0.59-0.74]), MoCA improvement within 3-6 months (OR = 0.79, 95% CI = [0.71-0.89], and within 1 year (OR = 0.86, 95% CI = [0.76-0.96]) were associated with long-term PSCI, while baseline MoCA (OR = 0.76, 95% CI = [0.61-0.96]) was also associated with incident PSCI. CONCLUSION: While most domains remained stable across-time, visual memory demonstrated an overall improvement. Short-term cognitive improvement could be an early indicator of long-term cognitive trajectory to identify individuals who may be resilient to PSCI.
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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) | 0.000 | 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".