Prevalence of Neuropsychiatric Symptoms in Young-Onset Compared to Late-Onset Alzheimer’s Disease – Part 1: Findings of the Two-Year Longitudinal NeedYD-Study
Bibliographic record
Abstract
BACKGROUND/AIMS: Knowledge about neuropsychiatric symptoms in young-onset Alzheimer's disease (YO-AD) is scarce, but essential to establish a prognosis and plan care for YO-AD patients. The aim of this study is to assess frequency parameters of neuropsychiatric symptoms in YO-AD over 2 years and investigate whether there are differences compared with late-onset Alzheimer's disease (LO-AD). METHODS: 98 YO-AD and 123 LO-AD patients and caregivers from two prospective cohort studies were included and assessed every 6 months for 2 years, using the Neuropsychiatric Inventory to evaluate neuropsychiatric symptoms. RESULTS: Over the course of 2 years, the incidence, prevalence and persistence of neuropsychiatric symptoms were in general lower in YO-AD than in LO-AD, specifically for delusions, agitation, depression, anxiety, apathy, irritability and aberrant motor behavior. Frequency of individual symptoms showed large variability in both groups. Within the group of YO-AD patients, apathy was the most prevalent symptom. CONCLUSION: Neuropsychiatric symptoms, notably apathy, should be paid appropriate attention to in the diagnosis and treatment of YO-AD patients. Further research is needed to gain insight into the mechanisms underlying the differences in neuropsychiatric symptoms between YO-AD and LO-AD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".