Do Depressive Symptoms Predict Alzheimer's Disease and Dementia?
Bibliographic record
Abstract
BACKGROUND: Depressive symptoms are common in seniors and may predict dementia. The objective of this study was to evaluate multiple measures of depressive symptoms to determine whether they predict subsequent Alzheimer's disease (AD) or dementia. METHODS: This population-based cohort study with 5-year follow-up included 766 community-dwelling seniors (ages 65+ years) in Manitoba, Canada. Measurements considered were the Center for Epidemiologic Studies Depression (CES-D) scale, participant-reported medical history, and duration of depression. RESULTS: Total CES-D score was a significant predictor of AD and dementia when categorized as a dichotomous variable according to the cutoff scores of 16 and 17; a CES-D cutoff of 21 was a significant predictor of AD and a marginally significant predictor of dementia. When analyzed as a continuous variable, CES-D score was marginally predictive of AD and dementia. Neither participant-reported history of depression nor participant-reported duration of depression was significant in predicting AD or dementia. CONCLUSION: Because depressive symptoms as measured by the CES-D predict the development of AD and dementia over 5 years, clinicians should monitor their older patients with these symptoms for signs of cognitive impairment.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".