The Correlation between Cognition Screening Scores and Gait Status from Three-Dimensional Gait Analysis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Gait impairment in patients with cognitive decline has received considerable attention over the past several decades. However, gait disturbance in dementia is often underdiagnosed. The Mini Mental State Examination (MMSE) is the most widely used screening test for dementia, and the Montreal Cognitive Assessment (MoCA) has been developed for more accurate assessments of mild cognitive impairment (MCI). The purpose of this study was to determine the correlation between gait status and the scores on these screening tests for dementia. METHODS: We recruited 18 patients with MCI and 19 patients with early-stage dementia. All of the participants were examined using the Korean versions of the MMSE and MoCA developed for screening dementia (MMSE-DS and MoCA-K, respectively) and a neuropsychological test to determine cognitive function. A three-dimensional motion-capture system was used to perform objective measurements of gait in all participants. We evaluated the correlation between the screening scores and gait parameters. RESULTS: =0.22) were not. The neuropsychological test revealed that walking speed and stride length were significantly correlated with memory and frontal lobe function. CONCLUSIONS: We found that the MoCA-K reflects the gait status in patients with cognitive decline more accurately than does the MMSE-DS. Our results suggest that the MoCA-K has more advantages than the MMSE-DS as a screening tool for dementia.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".