Donepezil for gait and falls in mild cognitive impairment: a randomized controlled trial
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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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".