Pharmacological Management of Agitation and Aggression in Alzheimer's Disease: A Review of Current and Novel Treatments
Bibliographic record
Abstract
Agitation and aggression are common neuropsychiatric symptoms of Alzheimer's disease and are highly prevalent in people with dementia. When pharmacological intervention becomes necessary, current clinical practice guidelines recommend antipsychotics, cholinesterase inhibitors, and some antidepressants. However, those interventions have modest to low efficacy, and those with the highest demonstrated efficacy have significant safety concerns. As a result, current research is focusing on novel compounds that have different mechanisms of action and that may have a better balance of efficacy over safety. The purpose of this review is to evaluate novel pharmacological therapies for the management of agitation and aggression in AD patients. We performed a comprehensive literature search to identify recent novel drugs that are not included in most clinical practice guidelines or are currently undergoing clinical trials for the treatment of agitation and/or aggression in AD. This review suggests that novel treatments, such as cannabinoids, lithium, non-steroidal anti-inflammatory drugs, analgesics, narcotics, and newer antiepileptic drugs, may provide a safer alternative treatment option for the management of agitation and aggression in AD and requires further study in order to clarify their risks and benefits.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".