Motor and Cognitive Trajectories Before Dementia: Results from Gait and Brain Study
Bibliographic record
Abstract
OBJECTIVES: To compare the trajectories of motor and cognitive decline in older adults who progress to dementia with the trajectories of those who do not. To evaluate the added value of measuring motor and cognitive decline longitudinally versus cross-sectionally for predicting dementia. DESIGN: Prospective cohort study with 5 years of follow-up. SETTING: Clinic based at a university hospital in London, Ontario, Canada. PARTICIPANTS: Community-dwelling participants aged 65 and older free of dementia at baseline (N=154). MEASUREMENTS: We evaluated trajectories in participants' motor performance using gait velocity and cognitive performance using the MoCA test twice a year for 5 years. We ascertained incident dementia risk using Cox regression models and attributable risk analyses. Analyses were adjusted using a time-dependent covariate. RESULTS: Overall, 14.3% progressed to dementia. The risk of dementia was almost 7 times as great for those whose gait velocity declined (hazard ratio (HR)=6.89, 95% confidence interval (CI)=2.18-21.75, p=.001), more than 3 times as great for those with cognitive decline (HR=3.61, 95% CI=1.28-10.13, p=.01), and almost 8 times as great in those with combined gait velocity and cognitive decline (HR=7.83, 95% CI=2.10-29.24, p=.002), with an attributable risk of 105 per 1,000 person years. Slow gait at baseline alone failed to predict dementia (HR=1.16, 95% CI=0.39-3.46, p=.79). CONCLUSION: Motor decline, assessed according to serial measures of gait velocity, had a higher attributable risk for incident dementia than did cognitive decline. A decline over time of both gait velocity and cognition had the highest attributable risk. A single time-point assessment was not sufficient to detect individuals at high risk of 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".