Benefits of combined cholinesterase inhibitor and memantine treatment in moderate–severe Alzheimer's disease
Bibliographic record
Abstract
BACKGROUND: Clinical studies and post hoc analyses have investigated the use of combination therapy for the treatment of Alzheimer's disease (AD). We review the evidence for the short- and long-term efficacy of combination therapy in AD. METHODS: The review is based on a search of the PubMed database to identify relevant articles concerning combination treatment with memantine and cholinesterase inhibitors (ChEIs). RESULTS: In patients with moderate-to-severe AD, combination treatment with the N-methyl-d-aspartate receptor antagonist memantine and the ChEI donepezil has produced significant benefits in cognition, function, behavior, global outcome, and care dependency, compared with donepezil treatment alone. Data from long-term observational studies support these findings. Compared with ChEI monotherapy, combination treatment slowed cognitive and functional decline (a 4-year sustained effect that appeared to increase over time) and reduced the risk of nursing home admission. Preclinically, the combination of N-methyl-d-aspartate receptor modulation and acetylcholinesterase inhibition has been shown to act synergistically, which may explain the observed clinical effects of combination treatment. CONCLUSION: Treatment with memantine/ChEI combination therapy in moderate-to-severe AD produces consistent benefits that appear to increase over time, and that are beyond those of ChEI treatment alone.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".