Donepezil for gait and falls in mild cognitive impairment: a randomized controlled trial
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Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Cognitive enhancers are commonly prescribed to people with Alzheimer's disease and related dementias to improve cognition and function. However, their effectiveness for individuals in the pre-stages of dementia, particularly in functional motor outcomes, remains unknown. We aimed to determine the efficacy of donepezil, a cognitive enhancer that improves cholinergic neurotransmission, on gait performance in mild cognitive impairment (MCI). METHODS: This was a double-blind, placebo-controlled trial including 60 older adults with MCI, randomized to receive donepezil (10 mg/daily, maximal dose) or placebo. Primary outcome was gait speed (cm/s) under single and three dual-task conditions (counting backwards by 1 or 7 and naming animals) measured using an electronic walkway. Dual-task gait cost (DTC), a valid measure of motor-cognitive interaction, was calculated as the percentage change between single (S) and dual-task (D) gait speeds: [(S - D)/S] × 100. Secondary outcomes included attention, executive function, balance and falls. RESULTS: After 6 months, the donepezil group experienced an improvement in dual-task gait speed (range 4-11 cm/s), although this was not statistically significant. The donepezil group showed a significant reduction in DTC (improvement) by counting backwards by 1 and 7 compared with placebo (10.25% vs. 1.75%, P = 0.048; 21.38% vs. 14.64%, P = 0.037, intention-to-treat analysis). Per-protocol analyses showed that all three DTCs improved in the donepezil group, along with a non-significant reduction of rate of falls. CONCLUSIONS: Donepezil treatment improved dual-task gait speed and DTC in elderly patients with MCI. Our results support the concept of reducing falls in MCI by targeting the motor-cognitive interface.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it