The Use of Risperidone in Behavioral and Psychological Symptoms of Dementia: A Review of Pharmacology, Clinical Evidence, Regulatory Approvals, and Off-Label Use
Bibliographic record
Abstract
Dementia represents a global health challenge due to the increase in elderly population worldwide. In addition to memory loss, dementia often results in severe behavioral and psychological changes where pharmacological treatments might be considered in addition to nonpharmacological strategies for optimal symptomatic control. Risperidone, the second oldest atypical antipsychotic, has been widely used off-label to treat behavioral and psychological symptoms of dementia (BPSD), including agitation, aggression, and psychosis. Several studies have indicated that risperidone offers a modest and statistically significant effectiveness in the clinical setting. However, in the past decade, safety concerns emerged due to increased risk for cerebrovascular adverse events and death following the use of risperidone in the elderly population. Clinical guidelines suggest that, in severe dementia where an older adult is threatening to harm himself or others, pharmacological treatments might be considered when nonpharmacological treatments fail. Risperidone was approved for BPSD in some countries (Australia, Canada, United Kingdom and New Zealand) but not in the United States. This article reviews risperidone's pharmacological activity, clinical effectiveness and safety, marketing approval, and off-label use in BPSD.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".