Psychosis in Alzheimer disease — mechanisms, genetics and therapeutic opportunities
Bibliographic record
Abstract
Psychosis is a common and distressing symptom in people with Alzheimer disease, and few safe and effective treatments are available. However, new approaches to symptom assessment and treatment are beginning to drive the field forward. New nosological perspectives have been provided by incorporating the emergence of psychotic symptoms in older adults - even in advance of dementia - into epidemiological and neurobiological frameworks as well as into diagnostic and research criteria such as the International Psychogeriatric Association criteria for psychosis in neurocognitive disorders, the Alzheimer's Association International Society to Advance Alzheimer's Research and Treatment (ISTAART) research criteria for psychosis in neurodegenerative disease, and the ISTAART criteria for mild behavioural impairment. Here, we highlight the latest findings in genomics, neuroimaging and neurobiology that are informing approaches to drug discovery and repurposing. Current pharmacological and non-pharmacological treatment options are discussed, with a focus on safety and precision medicine. We also explore trial data for pimavanserin, a novel agent that shows promise for the treatment of psychosis in people with dementia, and discuss existing agents that might be useful but need further exploration such as escitalopram, lithium, cholinesterase inhibitors and vitamin D. Although the assessment and management of psychosis in people with dementia remain challenging, new opportunities are providing direction and hope to the field.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".