Abstract T P128: Dual Task Walking Reveals Cognitive Dysfunction in Community-Dwelling Elderly Subjects: The Sefuri Brain MRI Study
Bibliographic record
Abstract
Introduction: The aim of our study was to investigate the effects of subclinical brain lesions and cognitive function on gait performance with or without concurrent cognitive task (i.e., dual task) in community-dwelling elderly subjects. Subjects and Methods: Between 2010 and 2012, magnetic resonance imaging (MRI) scans were performed for 170 non-demented volunteers (82 men and 88 women with a mean age of 67.5 years). The National Hospital Organization Hizen Psychiatric Center Institutional Review Board approved the study. All participants underwent neuropsychological tests (the Mini Mental State Examination [MMSE], Trail Making Test [TMT] and Rivermead Behavioral Memory Test [RBMT]). Time to complete the Timed Up and Go test was measured under two conditions: 1) Timed Up and Go test, and 2) dual task, walking while adding 3 to a predefined number “1”. Impaired gait velocity was defined as the most prolonged quintile in the time of single (>9.0 sec) and dual (>11.0 sec) tasks. We determined indices for atrophy using Voxcel-based Specific Regional Analysis for Alzheimer’s disease (VSRAD). Multivariate analysis was done using the logistic regression analysis with IBM SPSS Statistics 18 software for Windows. Results: Mean education years was 11.3±2.1 (S.D.) years, and mean MMSE score was 27.8±2.1. Silent brain infarction, deep white matter lesions (DWMLs), periventricular hyperintensities and cerebral microbleeds were detected in 19 (11.2%), 50 (29.4%), 24 (14.1%) and 8 (4.7%), respectively. Impaired gait velocity of single task (Timed Up and Go test) was associated with DWMLs (OR 2.57, 95% CI 1.16-5.69) after adjusted for age, sex, education, MMSE, TMT, RBMT, and MRI findings. Impaired gait velocity of the dual task walking was associated with age (OR 3.59/10 years, 95% CI 1.91-6.73) and score of the RBMT (OR 0.88, 95% CI 0.79-0.99), whereas MRI findings including brain atrophy (VSRAD) were not associated with reduced speed of dual task walking. Conclusions: Gait disturbance detected by single task was related to DWMLs, while dual task walking revealed an association between reduced gait speed and cognitive dysfunction. The present study showed that gait represents not only physical functioning but also cognitive dysfunction in healthy elderly individuals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 0.001 |
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".