Physical activities attenuate the negative cognitive impact from white matter hyperintensities in stroke and TIA patients with low education
Bibliographic record
Abstract
BACKGROUND: The objective of this study is to examine the effects of recent regular participation leisure activities upon cognitive functions between 3 and 6 months after stroke or transient ischemic attack (TIA). We also explored whether the cognitive effects interacted with the severity of white matter hyperintensities (WMH), a marker of cerebral white matter disease, in patients with low or high education. METHODS: Two-hundred and ninety-two subjects with mean age of 66.1 (11.0) years were recruited at median 161(131-180) days post index event. WMH volume was evaluated using a semi-automated method on MRI brain. Cognitive functions were measured using the Montreal Cognitive Assessment (MoCA). Multivariable linear regression analysis was conducted to explore the associations between leisure activity participation with WMH and the moderating effects of leisure activities upon relationship between WMH and MoCA. Analyses were further stratified by low (<6 years) or high education (≥6 years). All models were adjusted with age, sex, and years of education. RESULTS: Physical activity (PA), but not intellectual activity (IA), was negatively related to WMH volume (P < .05). IA exerted a main effect on MoCA performance (b = 3.21, P < .001). PA, but not IA, significantly interacted with WMH volume (b = -0.18, P < .01) on MoCA performance, but the interaction was only significant in the lower education group (b = 0.28, P < .01) but not in the higher education group. CONCLUSIONS: In patients with stroke/TIA, IA confers general cognitive benefits. Regular participation in PA negatively correlated with WMH volume. In patients with low education, PA increases resilience against vascular cognitive impairment.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".