DOES COGNITIVE RESERVE MODERATE THE RELATIONSHIP BETWEEN STROKE SEVERITY AND COGNITIVE FUNCTION?
Bibliographic record
Abstract
Abstract Cognitive decline is common after stroke. This study aimed to explore the moderating effect of cognitive reserve on the relationship between stroke severity and long-term cognitive function.A longitudinal survey of patients with acute ischemic stroke (AIS) was conducted in 2023 at four stroke centers in Nanjing and Shanghai, China. A total of 371 eligible patients were interviewed at acute stage and re-interviewed at 3 and 6 months after onset. Stroke severity was assessed using The National Institutes of Health Stroke Scale(NIHSS) at admission. Cognitive reserve was assessed by Cognitive Reserve Index questionnaire (CRIq) within 7 days after stroke onset. Cognitive function was assessed by Montreal Cognitive Assessment-Changsha Version(MoCA-CS) at each wave. A series of general linear mixed models were applied to test the moderating effect of cognitive reserve.The study found that cognitive function improved over time in all patients after stroke(β=1.314, p<0.001). The cognitive function of patients with higher NIHSS score was worse than those with lower NIHSS score(β=-1.315, p<0.01).Patients with a higher level of cognitive reserve had better cognitive function after stroke than those with a lower level of cognitive reserve(β=0.048, p<0.01). The interaction between NIHSS and cognitive reserve was statistically significant(β=0.012, p<0.05)after controlling for a covariate[Trial of ORG 10172 in Acute Stroke Treatment (TOAST)].Cognitive reserve can mitigate the impact of stroke burden on long-term cognitive decline in Chinese AIS patients to some extent. Cognitive reserve provides a potential target for the prevention and intervention of cognitive impairment after stroke.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".