Is cognitive reserve associated with cognitive function across stroke severity? A longitudinal study among Chinese stroke patients
Bibliographic record
Abstract
Background Cognitive decline is common after stroke. This study assessed the longitudinal associations between cognitive reserve and post-stroke cognitive function and tested whether these associations differ across patients’ stroke severity. Methods A longitudinal survey was conducted among 371 patients with acute ischemic stroke from four stroke centers in China from 2022 to 2023. Eligible patients were recruited at acute stage and followed up at 3 and 6 months after onset. Cognitive reserve was assessed by Cognitive Reserve Index questionnaire within 7 days after stroke onset. Cognitive function was assessed by Montreal Cognitive Assessment, Changsha Version at each time point. Stroke severity was assessed using the National Institutes of Health Stroke Scale at admission. Linear mixed models were applied to assess associations between cognitive reserve and cognitive function across stroke severity. Results Among stroke survivors, cognitive function improved over time after the onset ( β time = 0.40, p <0.001). Higher level of cognitive reserve was associated with better cognitive function ( β cognitive reserve = 0.08, p <0.001) after controlling for covariates. These associations remained over time ( β cognitive reserve time = 0.01, p = 0.009) and did not differ across patients with different stroke severity ( β cognitive reserve stroke severity = 0.01, p = 0.11). Conclusion Cognitive reserve can potentially mitigate the impact of stroke on long-term cognitive decline in Chinese patients with acute ischemic stroke. Targeting cognitive reserve may be a viable strategy to prevent or slow post-stroke cognitive decline. The study supports a more nuanced assessment of cognitive reserve as a standard in clinical studies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".