Revisiting cognitive reserve and cognition in multiple sclerosis: A closer look at depression
Bibliographic record
Abstract
BACKGROUND: The protective effect of cognitive reserve (CR) on cognition in people with multiple sclerosis (PwMS) has been well described. OBJECTIVE: To explore the relationship between aspects of CR, namely, leisure pursuits and depression. METHODS: In a cross-sectional study, a sample of 155 PwMS and 115 healthy controls (HC) underwent cognitive testing with the Minimal Assessment of Cognitive Function in Multiple Sclerosis (MACFIMS) battery. Leisure activity was retrospectively recorded using the Leisure Activity Scale (LAS). Depression was assessed using the Hospital Anxiety and Depression Scale. RESULTS: PwMS demonstrated greater decreases in leisure activity over time compared to the HC group, particularly in the past year ( p < 0.001). Here, depression accounted for 17% of the variance in determining the level of leisure activity ( p < 0.001). Premorbid IQ and leisure activity within the past year emerged as significant predictors of information processing speed, learning, memory and executive function. After controlling for depression, the influence of leisure activity on cognition was insignificant. CONCLUSION: Depression can cause significant changes in behaviour which can influence indices of CR, such as leisure pursuits. Successfully treating depression may lead to a more active lifestyle thereby offsetting in part the cognitive burden of disease.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".