Pharmacological Management of Agitation and Aggression in Alzheimer's Disease: A Review of Current and Novel Treatments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it