Brief operationalized psychosocial interventions for agitation and psychosis: Use in clinical trials and clinical care
Bibliographic record
Abstract
BACKGROUND: Neuropsychiatric symptoms such as agitation and/or psychosis impact most individuals with dementia during their illness, leading to reduced quality of life, distress, more rapid clinical decline, and an increased risk of institutionalization. Widely used pharmacological approaches have modest benefits and are associated with significant adverse events. METHODS: A narrative review was conducted to examine the evidence for the benefits of non-pharmacological treatments for agitation and psychosis in people with dementia, with a focus on operationalized approaches that could readily be introduced into clinical practice. In the absence of substantial evidence pertaining to non-pharmacological treatments of psychosis, the review was supplemented with a secondary analysis of an existing WHELD/BPST dataset. RESULTS: There is substantial evidence that simple non-pharmacological treatment approaches, such as personalized activities with social interaction, are effective in the treatment of agitation or enabling the reduction of psychotropic medication without worsening of agitation. Examples are presented of several operationalized approaches suitable for clinical implementation. In contrast, the treatment response of psychotic symptoms in people with dementia to non-pharmacological approaches is less clear cut. There is emerging evidence that although currently used non-pharmacological approaches do not directly improve psychosis in people with dementia, they do improve quality of life and concurrent neuropsychiatric symptoms such as apathy and agitation. DISCUSSION: Although best practice guidelines universally recommend non-pharmacological interventions as the first-line treatment for neuropsychiatric symptoms, implementation has been limited. Several tools, such as Brief Psychosocial Therapy, are described which may help bridge this gap into clinical practice.
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How this classification was reachedexpand
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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".