Complex Gait Is Related to Cognitive Functioning in Older People: A Cross-Sectional Study Providing an Innovative Test
Bibliographic record
Abstract
BACKGROUND: Many studies have demonstrated an inverse relationship between gait performance and cognitive impairment. The main purposes of this study were: (1) to design and validate a complex gait test (CGT) in older people, (2) to analyze the effects of age and sex on CGT, and (3) to analyze the association between CGT performance and physical functioning and cognitive measures. METHODS: A total of 279 older people (60-97 years) were analyzed in 2019. Fitness tests, gait performance, and several cognitive measures such asthe Trail-Walking Test and Montreal Cognitive Assessment were used. RESULTS: The CGT reported adequate reliability and validity parameters. In the test-retest analysis, the intra-class correlation coefficient was 0.868 (p < 0.001). There was a significant correlation between the CGT and Trail-Walking Test (r = 0.592; p < 0.001). The linear regression analysis showed that the CGT was associated with the Montreal Cognitive Assessment (R2 = 0.357; p =0.001). The binary logistic regression analysis revealed that a high CGT score was a risk factor for mild cognitive impairment (odds ratio 1.201, 95% CI 1.081-1.334; p = 0.001). The ROC curve of the mild cognitive impairment was predicted by the CGT performance (area under the curve = 0.768, 95% CI 0.647-0.889; p < 0.001), reaching the cut-off point at 20.25 s. CONCLUSIONS: The CGT showed good reliability and validity and may serve as a potential biomarker in mild cognitive impairment prediction in older adults aged 60-97 years.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".