Neuropsychiatric Symptoms Present Differently in Individuals with Different High-Risk States of Dementia
Bibliographic record
Abstract
Introduction: Neuropsychiatric symptoms (NPS) are common in neurocognitive disorders. However, the differences in presentation of NPS in high-risk states for dementia such as mild neurocognitive disorder (Mild NCD) and remitted major depressive disorder (rMDD) remain unclear. The purpose of this study was to compare the frequency and factor structure of NPS in Mild NCD, rMDD, and Mild NCD with rMDD (Mild NCD-rMDD). METHODS: We analyzed baseline data from the multicenter Prevention of Alzheimer's Dementia with Cognitive Remediation plus Transcranial Direct Current Stimulation in Mild Cognitive Impairment and Depression trial (NCT0238667). NPS were assessed using the Neuropsychiatric Inventory Questionnaire in those with Mild NCD, rMDD, and Mild NCD-rMDD. We compared the NPS frequency and factor structure across the three groups. RESULTS: Among 374 participants with a mean (SD) age = 72.0 (6.3) years, the overall frequency of any NPS was highest in Mild NCD-rMDD (75.9%), as compared to Mild NCD (63.5%) or rMDD (55.7%) groups (p = 0.014). Depression/dysphoria was the most common NPS in all three groups. In factor analyses, NPS grouped into four factor structures in all three groups, but the composition of factors of individual symptoms (delusions, motor disturbances, nighttime behaviors, anxiety, and apathy) were different. CONCLUSION: NPS are common in high-risk states of dementia, and the frequency of NPS is higher in Mild NCD-rMDD as compared to only Mild NCD or rMDD. Further, there are key differences in presentation of NPS in Mild NCD, rMDD, and Mild NCD-rMDD. Future studies should investigate the relevance of these differences for cognition, function, and disease biomarkers. .
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".