Association of Posture, Gait, and Auditory Functioning with Cognitive Status in a Cohort of Community-Dwelling Older Adults
Bibliographic record
Abstract
Postural stability, hearing, and gait function deterioration are the risk factors associated with cognitive impairment. Although no method has been reported for treating severe cognitive impairment to date, developing an early detection model based on these risk factors could aid in slowing down or even reversing the deterioration process. In this study, the association between cognitive impairment and the combined predictive ability of sensory and gait features was assessed. Fifty-seven healthy community-dwelling men over the age of sixty-five participated in cognitive, postural stability, auditory, and level walking evaluations. They were divided into two groups: healthy control group (n = 39) and lower cognition group (n = 18), based on their Montreal cognitive assessment score. During gait, the center of mass of the cognitively impaired participants was confined to a smaller volume. Furthermore, the cognitively healthy participants were found to have better postural stability. Both groups possessed similar hearing ability; however, the cognitively impaired group made a significantly higher number of errors when repeating words or sentences. A logistic regression model utilizing each of these function quantifiers exhibited a high area under the receiver operating characteristic curve, suggesting excellent predictive ability. These models can be applied to smartphone or smart home healthcare technologies to detect the possibility of cognitive impairment, thus facilitating early detection.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".