Cognitive reserve and executive functions in dual task gait performance in Parkinson’s disease
Bibliographic record
Abstract
A higher level of education was correlated with less severe motor impairment in Parkinson's Disease (PD). Nevertheless, there is limited evidence on the relationship between cognitive reserve and motor performance in complex situations in PD. To investigate the association between cognitive reserve and the dual-task gait effect in PD. Additionally, we examined the relationship between executive function, clinical and sociodemographic variables and, dual-task gait effects. We conducted a cross-sectional study with 44 PD participants. We evaluated dual-task effect on cadence, stride length, and gait velocity. Dual-task effects were correlated with neurophysiological factors, including cognitive reserve (Cognitive Reserve Index Questionnaire), overall cognitive performance of executive functions, a specific executive function domain (Trail Making Test), and the global cognitive status (Montreal Cognitive Assessment and Mini-Mental State Examination). Age, gender, and disease severity were considered as variables to be examined for correlation. We found that cognitive reserve did not influence gait performance under dual-task conditions in this sample. However, executive functions, age, and disease severity were associated with the dual-task effect on gait. The overall cognitive performance with respect to the Trail Making Test showed an inverse relationship in the dual-task gait effect on cadence. Our study's findings have important implications for understanding the association between executive functions, age, and disease severity with the dual-task effect on gait in PD. Pre-life factors, such as education, occupation, and leisure activity, did not contribute to coping with complex gait situations in PD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".