Memantine in dementia: a review of the current evidence
Bibliographic record
Abstract
INTRODUCTION: As the world's population ages, the incidence of Alzheimer's disease (AD) is projected to double every 20 years. Understanding the pathogenesis of AD and developing effective treatments is a public health imperative. Memantine is a low- to moderate-affinity, non-competitive NMDA receptor antagonist that is currently approved for the treatment of moderate to severe AD. AREAS COVERED: We discuss the current evidence, emphasizing more recent studies examining the effects of memantine in AD. We also look at the gaps in the current knowledge; the studies that will be required to fill these gaps are also discussed. The present paper reviews: the pharmacology of memantine; evidence for its use in moderate to severe AD, as well as in mild to moderate AD; adverse events related to memantine use; its effects specifically on behaviours including aggression and agitation; the pharmacoeconomics of memantine; and the use of memantine in other dementias. Memantine has shown modest benefits in cognition, function, global and behavioural measures, and has shown little potential for drug-drug interactions. EXPERT OPINION: For the treatment of moderate to severe AD, memantine should be offered as a therapeutic option, either on its own, or in combination with a cholinesterase inhibitor.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".