Apathy and cognitive and functional decline in community-dwelling older adults: results from the Baltimore ECA longitudinal study
Bibliographic record
Abstract
BACKGROUND: Apathy, a complex neuropsychiatric syndrome, commonly affects patients with Alzheimer's disease. Prevalence estimates for apathy range widely and are based on cross-sectional data and/or clinic samples. This study examines the relationships between apathy and cognitive and functional declines in non-depressed community-based older adults. METHODS: Data on 1,136 community-dwelling adults aged 50 years and older from the Baltimore Epidemiologic Catchment Area (ECA) study, with 1 and 13 years of follow-up, were used. Apathy was assessed with a subscale of items from the General Health Questionnaire. Logistic regression, t-tests, chi2 and Generalized Estimating Equations were used to accomplish the study's objectives. RESULTS: The prevalence of apathy at Wave 1 was 23.7%. Compared to those without, individuals with apathy were on average older, more likely to be female, and have lower Mini-mental State Examination (MMSE) scores and impairments in basic and instrumental functioning at baseline. Apathy was significantly associated with cognitive decline (OR = 1.65, 95% CI = 1.06, 2.60) and declines in instrumental (OR = 4.42; 95% CI = 2.65, 7.38) and basic (OR = 2.74; 95%CI = 1.35, 5.57) function at 1-year follow-up, even after adjustment for baseline age, level of education, race, and depression at follow-up. At 13 years of follow-up, apathetic individuals were not at greater risk for cognitive decline but were twice as likely to have functional decline. Incidence of apathy at 1-year follow up and 13-year follow-up was 22.6% and 29.4%, respectively. CONCLUSIONS: These results underline the public health importance of apathy and the need for further population-based studies in this area.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".