Pharmacologic Management of Neuropsychiatric Symptoms of Alzheimer Disease
Bibliographic record
Abstract
OBJECTIVE: To systematically review published clinical trials of the pharmacotherapy of neuropsychiatric symptoms of Alzheimer disease (AD). METHOD: We searched MEDLINE and EMBASE for published English-language medical literature. Our review focused on randomized controlled trials (RCTs) and corresponding metaanalyses. RESULTS: The pharmacotherapy of neuropsychiatric symptoms of AD has been studied with numerous RCTs. The largest number of studies has focused on antipsychotics. Data are of reasonably high quality and indicate that risperidone and olanzapine are more effective than placebo for institutionalized patients with severe agitation, aggression, and psychosis. The efficacy of antipsychotics is counterbalanced by safety concerns that include cerebrovascular adverse events and mortality. Cholinesterase inhibitors and memantine appear to have modest benefits for patients with mildly to moderately severe symptoms. Antidepressants are effective for treating depression in AD, but more data are required to determine the efficacy of trazodone and citalopram for agitation and aggression. Carbamazepine appears to be efficacious, although side effects and concerns about drug-drug interactions limit its use. The data do not support the use of valproate. Benzodiazepines should only be used for short-term, as-needed use. There are insufficient data on other pharmacologic interventions, such as beta blockers, buspirone, and estrogen preparations. CONCLUSIONS: Although there have been numerous well-designed studies of the pharmacotherapy of neuropsychiatric symptoms in AD, safer and more effective treatments are urgently needed.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".