Neuropsychiatric Symptom Burden across Neurodegenerative Disorders and its Association with Function
Bibliographic record
Abstract
Objective Neuropsychiatric symptoms (NPS) are prevalent in neurodegenerative disorders, however, their frequency and impact on function across different disorders is not well understood. We compared the frequency and severity of NPS across Alzheimer's disease (AD) (either with mild cognitive impairment or dementia), Cerebrovascular disease (CVD), Parkinson's disease (PD), frontotemporal dementia (FTD), and amyotrophic lateral sclerosis (ALS), and explored the association between NPS burden and function. Methods We obtained data from Ontario Neurodegenerative Disease Research Initiative (ONDRI) that included following cohorts: AD ( N = 111), CVD ( N = 148), PD ( N = 136), FTD ( N = 50) and ALS ( N = 36). We compared the frequency and severity of individual NPS (assessed by the neuropsychiatric inventory questionnaire) across cohorts using generalized estimating equations and analysis of variance. Second, we assessed the relationship of NPS burden with instrumental (iADLs) and basic (ADLs) activities of living across cohorts using multivariate linear regression while adjusting for relevant demographic and clinical covariates. Results Frequency of NPS varied across cohorts (χ 2 (4) = 34.4, p < .001), with post-hoc tests showing that FTD had the greatest frequency as compared to all other cohorts. The FTD cohort also had the greatest severity of NPS ( H (4) = 34.5, p < .001). Further, there were differences among cohorts in terms of the association between NPS burden and ADLs ( F (4,461) = 3.1, p = 0.02). Post-hoc comparisons suggested that this finding was driven by the FTD group, however, the differences did not remain significant following Bonferroni correction. There were no differences among cohorts in terms of the association between NPS burden and IADLs. Conclusions NPS frequency and severity are markedly greater in FTD as compared to other neurodegenerative diseases. Further, NPS burden appears to be associated differently with function across neurodegenerative disorders, highlighting the need for individualized clinical 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".