Can Depressive Symptomatology at Diagnosis Predict Cognitive and Functional Decline Over 1 Year in Rural Canadian Patients With Dementia?
Bibliographic record
Abstract
INTRODUCTION: Depressive symptomatology is often associated with the onset of dementia, although the exact form and directionality of this association is still unclear. The aim of this study is to investigate whether depressive symptomatology at the time of dementia diagnosis was predictive of cognitive, functional, and behavioral decline over 1 year. METHODS: In a Rural and Remote Memory Clinic, 375 patients consecutively diagnosed with mild cognitive impairment, Alzheimer disease, or non-Alzheimer disease dementia completed the Center for Epidemiological Studies Depression Scale at first visit and 1-year follow-up to assess depressive symptomatology. The same cohort was evaluated for cognitive, functional, and behavioral decline through the completion of 5 clinical tests performed at the first visit and at 1-year follow-up. RESULTS: Depressive symptomatology at time of dementia diagnosis did not predict cognitive or functional decline over 1 year, although increases in depressive symptomatology over 1 year significantly correlated with higher caregiver ratings of neuropsychiatric symptom severity and related distress over that time. CONCLUSION: Increasingly severe depressive symptomatology over 1 year correlated with greater caregiver distress. This study points the way for future studies delineating the relationship between depression, dementia progression, and caregiver distress.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".