Natural Evolution and Heterogeneity of Neuropsychiatric Symptoms Across Neurocognitive Disorders: A Systematic Review and Meta‐Analysis of Longitudinal Studies
Bibliographic record
Abstract
OBJECTIVES: Neuropsychiatric symptoms (NPS) are common and burdensome manifestations of neurocognitive disorders but the characterization of their natural evolution over time is limited. We conducted a systematic review and meta-analysis to describe the longitudinal progression and heterogeneity of NPS across Alzheimer's disease (AD), Lewy body dementia (LBD), and frontotemporal dementia (FTD). METHODS: We systematically reviewed observational longitudinal studies published between 2000 and 2024 assessing the prevalence or severity of NPS at ≥ 2 time points using validated instruments. Random-effects meta-analyses were performed to pool changes in prevalence (risk difference) and severity (standardized mean difference [SMD]) at short-term (< 12 months), mid-term (12 to < 24 months), and long-term (≥ 24 months) follow-up. Heterogeneity and publication bias were also assessed. Subgroup analyses were conducted by type of neurocognitive disorder. RESULTS: We included 26 studies comprising 5509 participants. Apathy was the most prevalent NPS at baseline (45%), followed by irritability (35%) and depression (35%). Over time, most NPS demonstrated progressive increases in prevalence, with agitation (+16%) and apathy (+15%) emerging most frequently at long-term follow-up. Severity of most NPS remained stable, except for apathy and aberrant motor behavior, which increased significantly, and sleep disturbances, which improved. Global NPS burden increased significantly at long-term (SMD +2.04). Heterogeneity of trajectories was notably high and varied across symptoms and disorders. Subgroup analyses in AD confirmed increases in apathy, delusions, and hallucinations over time. CONCLUSIONS: NPS exhibit distinct and heterogeneous longitudinal patterns in neurocognitive disorders. Greater understanding of these trajectories may inform clinical management, prognostication, and the timing of interventions.
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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 | 0.030 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.042 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".