Gait performance in older adults across the cognitive spectrum: Results from the <scp>GAIT</scp> cohort
Bibliographic record
Abstract
BACKGROUND: Gait performance can provide valuable insights into cognitive functioning in older adult and may be used to screen for cognitive impairment. However, the optimal test condition and spatiotemporal parameter for accuracy have not yet been determined. This study aims to determine the gait measure with the highest accuracy identifying cognitive decline. METHODS: A total of 711 participants were recruited, including 332 cognitively healthy individuals, 264 with mild cognitive impairment (MCI), and 115 with dementia, with a mean age of 72 years (interquartile range 69-76), and 43% (n = 307) of women. The participants underwent gait assessment in three different conditions, including a single task and dual tasks of counting backward by ones and naming animals. RESULTS: Gait performance was deteriorated as cognitive impairment progressed. The gait test performed during naming animals condition was the most accurate in differentiating between cognitive groups. Specifically, the naming animals gait speed was more accurate in discriminating control participants from those with cognitive impairment (area under the curve [AUC] = 76.9% for MCI and 99.7% for people with dementia with control group as reference). The coefficient of stride length variability while naming animals was the most effective parameter in discriminating between MCI and dementia groups (AUC = 96.7%). CONCLUSIONS: The naming animals dual-task gait test can be a valuable assessment for screening cognitive impairment in older adults, regardless of their cognitive abilities. The test is useful in clinical settings for subjects with a range of cognitive profiles.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".