Nonpharmacological Treatment of Alzheimer Disease
Bibliographic record
Abstract
OBJECTIVE: To review the key nonpharmacological treatment approaches to the cognitive and functional symptoms of Alzheimer disease (AD). METHODS: We searched and critically analyzed the most recent relevant literature pertaining to the nonpharmacological treatment of AD. RESULTS: There is evidence from a modest number of well-conducted randomized controlled trials (RCTs) that various nonpharmacological approaches, including cognitive training, cognitive rehabilitation, and cognitive stimulation therapy (CST), confer modest but significant benefits in the treatment of cognitive symptoms in people with AD, and that there may be additive benefits in combination with cholinesterase inhibitor therapy. Cognitive rehabilitation also appears to result in functional benefits in AD. The modest number of RCTs focusing on cognitive training in AD is consistent with the results of larger cognitive training trials in healthy older people. however, there is no convincing evidence of any benefits associated with brain training games. CONCLUSION: An emerging evidence base indicates that different approaches to cognitive training and cognitive stimulation in people with AD confer modest but significant benefits. The best evidence base is for CST, although this approach is labour-intensive, and requires further evaluation of cost-effectiveness. There is currently no evidence that brain training games provide any significant benefit to people with AD.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".