Spatiotemporal Gait Characteristics Associated with Cognitive Impairment: A Multicenter Cross-Sectional Study, the Intercontinental “Gait, cOgnitiOn & Decline” Initiative
Bibliographic record
Abstract
BACKGROUND: The study aims to determine the spatiotemporal gait parameters and/or their combination(s) that best differentiate between cognitively healthy individuals (CHI), patients with mild cognitive impairment (MCI) and those with mild and moderate dementia, regardless of the etiology of cognitive impairment. METHODS: A total of 2099 participants (1015 CHI, 478 patients with MCI, 331 patients with mild dementia and 275 with moderate dementia) were selected from the intercontinental "Gait, cOgnitiOn & Decline" (GOOD) initiative, which merged different databases from seven cross-sectional studies. Mean values and coefficients of variation (CoV) of spatiotemporal gait parameters were recorded during usual walking with the GAITRite® system. RESULTS: The severity of cognitive impairment was associated with worse performance on all gait parameters. Stride velocity had the strongest association with cognitive impairment, regardless of cognitive status. High mean value and CoV of stride length characterized moderate dementia, whereas increased CoV of stride time was specific to MCI status. CONCLUSION: The findings support the existence of specific cognitive impairment-related gait disturbances with differences related to stages of cognitive impairment, which may be used to screen individuals with cognitive impairment.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".