Updates and future perspectives on neuropsychiatric symptoms in Alzheimer's disease
Bibliographic record
Abstract
Neuropsychiatric symptoms (NPS) are common throughout the Alzheimer's disease (AD) continuum and profoundly affect patients, caregivers, and health-care systems. This review synthesizes key research presented in the 2022 and 2023 Alzheimer's Association International Society to Advance Alzheimer's Research and Treatment Neuropsychiatric Syndromes-Professional Interest Area (NPS-PIA) Year-In-Reviews, emphasizing six critical areas: (1) diversity and disparities, (2) diagnostic frameworks, (3) neurobiology of NPS, (4) NPS as a disease marker, (5) the impact of COVID-19, and (6) interventions. NPS accelerates AD progression, increases functional decline, diminishes quality of life, and heightens caregiver burden and institutionalization rates. Current treatments primarily rely on psychotropics, which offer limited efficacy and raise safety concerns. This review aims to inform clinicians and researchers about recent NPS advancements while identifying gaps for future studies to improve outcomes for individuals with AD. HIGHLIGHTS: Research in Alzheimer's disease-related neuropsychiatric symptoms has rapidly increased, indicating heightened interest. Key areas include: diversity, diagnostics, markers, COVID-19 impact, and treatments. A road map for future studies, based on the key areas of research, is provided. This road map includes considerations to improve study applicability and validity.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".