Neuropsychiatric Impairments as Predictors of Mild Cognitive Impairment, Dementia, and Alzheimer's Disease
Bibliographic record
Abstract
In this study, the relations between cognitive status and neuropsychiatric impairments in nondemented older adults in cross section and over time is examined. Using data from the Canadian Study of Health and Aging (CSHA), a longitudinal, nation-wide study in which data were collected 3 times (ie, CSHA-1, CSHA-2, CSHA-3) at 5-year intervals, individuals were classified with (n = 240) and without (n = 386) cognitive impairment at CSHA-2. Loss of interest, changes in personality and mood, and depression were reported by a knowledgeable informant (ie, family or friends) more frequently for those with cognitive impairment than for those without cognitive impairment. After controlling for initial cognitive status, loss of interest and depression contributed significantly to the prediction of mild cognitive impairment, dementia, and Alzheimer's disease over time. These findings suggest that these neuropsychiatric impairments play significant roles throughout the course of cognitive decline and should be taken into consideration even before cognitive impairment is evident.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".