Gabapentin and pregabalin to treat aggressivity in dementia: a systematic review and illustrative case report
Bibliographic record
Abstract
Aims The prevalence of dementia is rising as life expectancy increases globally. Behavioural and psychological symptoms of dementia (BPSD), including agitation and aggression, are common, presenting a challenge to clinicians and caregivers. Methods Following PRISMA guidelines, we systematically reviewed evidence for gabapentin and pregabalin against BPSD symptoms of agitation or aggression in any dementia, using six databases (Pubmed, CINHL, PsychINFO, HealthStar, Embase, and Web of Science). Complementing this formal systematic review, an illustrative case of a patient with BPSD in mixed Alzheimer's/vascular dementia, who appeared to derive benefits in terms of symptom control and functioning from the introduction of gabapentin titrated up to 3600 mg day−1 alongside other interventions, is presented. Results Twenty‐four relevant articles were identified in the systematic review. There were no randomized trials. Fifteen papers were original case series/case reports of patients treated with these compounds, encompassing 87 patients given gabapentin and six given pregabalin. In 12 of 15 papers, drug treatment was effective in the majority of cases. The remaining nine papers were solely reviews, of which two were described as systematic but predated PRISMA guidelines. Preliminary low‐grade evidence based on case series and case reviews suggests possible benefit of gabapentin and pregabalin in patients with BPSD in Alzheimer's disease. These benefits cannot be confirmed until well‐powered randomized controlled trials are undertaken. Evidence in frontotemporal dementia is lacking. Conclusion Gabapentin and pregabalin could be considered for BPSD when medications having stronger evidence bases (risperidone, other antipsychotics, carbamazepine and citalopram) have been ineffective or present unacceptable risks of adverse outcomes.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".