Are Cognitive Subtypes Associated with Dual-Task Gait Performance in a Clinical Setting?
Bibliographic record
Abstract
BACKGROUND: Poor dual-task gait (walking while performing a cognitively demanding task) has been linked to progression to dementia in older adults with mild cognitive impairment (MCI). However, many of these findings come from research environments; gait performance across the cognitive spectrum has not previously been studied in a clinical setting. OBJECTIVE: To examine whether patients from a memory clinic show differences in usual and dual-task gait speed and dual-task cost (DTC) based on cognitive diagnosis. METHODS: Patients in the Aging Brain and Memory Clinic (London, ON) performed a usual gait walk and three dual-task gait walks: counting backwards by ones, naming animals, and counting backwards by seven (serial sevens) out loud. Patients were timed with a stopwatch over a six-meter path marked on the floor. One-way ANOVA was performed to evaluate associations between gait speed and DTC (%) across groups. RESULTS: One hundred ninety-four patients with subjective cognitive impairment (SCI; n = 46), MCI (n = 77), or dementia (n = 71) were assessed. Performance in usual (p < 0.001) and dual-task gait speed (counting gait p < 0.001; naming animals p < 0.001; serial sevens p = 0.004) decreased across the spectrum of cognitive impairment. Patients with dementia had significantly higher DTC in both counting gait (p = 0.02) and naming animals (p = 0.04) conditions compared with patients with SCI and MCI, who had statistically similar DTC in all conditions. CONCLUSION: Dual-task gait performance significantly declines across the cognitive spectrum in a clinical setting. Dual-task gait testing may be used in conjunction with traditional assessments for diagnosing cognitive impairments.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".