Pharmacological treatments for neuropsychiatric symptoms of dementia in long-term care: a systematic review
Bibliographic record
Abstract
BACKGROUND: Medications are frequently prescribed for neuropsychiatric symptoms (NPS) associated with dementia, although information on the efficacy and safety of medications for NPS specifically in long-term care (LTC) settings is limited. The objective of this study was to provide a current review of the efficacy and safety of pharmacological treatments for NPS in LTC. METHODS: We searched MEDLINE, EMBASE, PsychINFO, and the Cochrane Library for randomized controlled trials comparing medications with either placebo or other interventions in LTC. Study quality was described using the Cochrane collaboration risk of bias tool. The efficacy of medications was evaluated using NPS symptom rating scales. Safety was evaluated through rates of trial withdrawals, trial withdrawals due to adverse events, and mortality. RESULTS: A total of 29 studies met inclusion criteria. The most common medications evaluated in studies were atypical antipsychotics (N = 15), typical antipsychotics (N = 7), anticonvulsants (N = 4), and cholinesterase inhibitors (N = 3). Statistically significant improvements in NPS were noted in some studies evaluating risperidone, olanzapine, and single studies of aripiprazole, carbamazepine, estrogen, cyproterone, propranolol, and prazosin. Study quality was difficult to rate in many cases due to incomplete reporting of details. Some studies reported higher rates of trial withdrawals, adverse events, and mortality associated with medications. CONCLUSIONS: We conclude that there is limited evidence to support the use of some atypical antipsychotics and other medications for NPS in LTC populations. However, the generally modest efficacy and risks of adverse events highlight the need for the development of safe and effective pharmacological and non-pharmacological interventions for this population.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".