Sequential drug treatment algorithm for agitation and aggression in Alzheimer’s and mixed dementia
Bibliographic record
Abstract
INTRODUCTION: Behavioural and psychological symptoms of dementia (BPSD) include agitation and aggression in people with dementia. BPSD is common on inpatient psychogeriatric units and may prevent individuals from living at home or in residential/nursing home settings. Several drugs and non-pharmacological treatments have been shown to be effective in reducing behavioural and psychological symptoms of dementia. Algorithmic treatment may address the challenge of synthesizing this evidence-based knowledge. METHODS: A multidisciplinary team created evidence-based algorithms for the treatment of behavioural and psychological symptoms of dementia. We present drug treatment algorithms for agitation and aggression associated with Alzheimer's and mixed Alzheimer's/vascular dementia. Drugs were appraised by psychiatrists based on strength of evidence of efficacy, time to onset of clinical effect, tolerability, ease of use, and efficacy for indications other than behavioural and psychological symptoms of dementia. RESULTS: After baseline assessment and discontinuation of potentially exacerbating medications, sequential trials are recommended with risperidone, aripiprazole or quetiapine, carbamazepine, citalopram, gabapentin, and prazosin. Titration schedules are proposed, with adjustments for frailty. Additional guidance is given on use of electroconvulsive therapy, optimization of existing cholinesterase inhibitors/memantine, and use of pro re nata medications. CONCLUSION: This algorithm-based approach for drug treatment of agitation/aggression in Alzheimer's/mixed dementia has been implemented in several Canadian Hospital Inpatient Units. Impact should be assessed in future research.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".