Comparing the distribution of neuropsychiatric symptoms among individuals with depression and mild cognitive impairment
Bibliographic record
Abstract
Abstract Background Neuropsychiatric symptoms (NPS) are common during the course of neurocognitive disorders. NPS have been previously reported in early and late stages of Alzheimer’s Disease. However, our understanding of NPS in high‐risk states for dementia such as mild cognitive impairment (MCI) and major depressive disorder (MDD) is poor. The purpose of this study was to compare the frequency and factor structure of neuropsychiatric symptoms among individuals with Mild Cognitive Impairment (MCI), Major Depressive Disorder (MDD) in remission, and comorbid MCI and MDD (in remission) (MCI‐D). Method We used baseline data from the Prevention of Alzheimer’s Dementia with Cognitive Remediation Plus Transcranial Direct Current Stimulation in Mild Cognitive Impairment and Depression (PACt‐MD) study, a multicenter trial across five academic sites in Toronto, Canada (clinical trial No. NCT0238667). We used ANOVA or χ2‐test to compare frequency of NPS across groups. We used factor analysis of Neuropsychiatric Inventory Questionnaire (NPI‐Q) items in the three groups. Result We included 374 participants with a mean age of 72.0 years (SD = 6.3). In the overall sample, at least one NPS was present in 64.2% participants, and 36.1% had at least moderate severity NPS (36.1%). Depression (54%, χ2 < 0.001) and apathy (28.7%, χ2 = 0.002) were more prevalent in the MCI‐D group as compared to MCI and MDD groups. In factor analysis, NPS grouped differently in MCI, MDD, and MCI‐D groups. A “psychotic” subgroup emerged among MCI and MCI‐D, but not in MDD. Night‐time behaviors and disinhibition grouped differently across all three groups. Conclusion Prevalence of NPS seems higher in persons with MCI‐D as compared to those with only MCI or MDD. The factor structure of NPS differed between MCI, MDD, and MCI‐D groups. Future studies should investigate the association of NPS factors with cognition, function, and illness 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".