Prevalence and Covariates of Elevated Depressive Symptoms in Rural Memory Clinic Patients with Mild Cognitive Impairment or Dementia
Bibliographic record
Abstract
BACKGROUND/AIMS: To estimate the prevalence, severity, and covariates of depressive symptoms in rural memory clinic patients diagnosed with either mild cognitive impairment (MCI) or dementia. METHODS: In a cross-sectional study of 216 rural individuals who attended an interdisciplinary memory clinic between March 2004 and July 2012, 51 patients were diagnosed with MCI and 165 with either dementia due to Alzheimer's disease (AD) or non-AD dementia. The Center for Epidemiologic Studies of Depression Scale (CES-D) was used to estimate the severity and prevalence of clinically elevated depressive symptomatology. RESULTS: The prevalence of elevated depressive symptoms was 51.0% in the MCI patients and 30.9% in the dementia patients. Depressive symptoms were more severe in the MCI patients than in the dementia patients. Elevated depressive symptoms were statistically associated with younger age for the MCI group, with lower self-rated memory for the dementia group, and with increased alcohol use and lower quality of life ratings for all patients. In the logistic regression models, elevated depressive symptoms remained negatively associated with self-rated memory and quality of life for the patients with dementia, but significant bivariate associations did not persist in the MCI group. CONCLUSIONS: The high prevalence and severity of depressive symptoms among rural memory clinic patients diagnosed with either MCI or dementia warrant continued investigation.
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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 | 0.000 | 0.002 |
| 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".