The moderating effect of cognitive reserve on cognitive function in patients with Acute Ischemic Stroke
Bibliographic record
Abstract
Background: Recovery of cognitive function after stroke has inter-individual variability. The theory of cognitive reserve offers a potential explanation of the variability in cognitive function after stroke. Objective: This study aimed to investigate the moderating effect of cognitive reserve on the relationship between the stroke severity and cognitive function after stroke. Materials and methods: A total of 220 patients with Acute Ischemic Stroke (AIS) were recruited in 2021 from two stroke centers in Nanjing, China. The National Institutes of Health Stroke Scale (NIHSS) was used to assess stroke severity upon admission. Cognitive Reserve Index questionnaire (CRIq) and validated Montreal Cognitive Assessment, Changsha Version (MoCA-CS) were used to assess cognitive reserve and cognitive function within 7 days after stroke onset, respectively. A series of multivariate linear regression models were applied to test the moderating effect of cognitive reserve. Results: = 0.045) after adjusting for some key covariates [e.g., age, marital status, Oxfordshire Community Stroke Project (OCSP) classification, Trial of ORG 10172 in Acute Stroke Treatment (TOAST) classification, cerebral vascular stenosis, diabetes and atrial fibrillation]. Conclusion: Cognitive reserve may help to buffer the effect of stroke-related pathology on cognitive decline in Chinese acute stroke patients. Enhancing cognitive reserve in stroke patients may be one of the potential strategies for preventing vascular dementia.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".