Characterization of Depressive Symptoms in Dementia and Examination of Possible Risk Factors
Bibliographic record
Abstract
Background: Depression in individuals with Alzheimer's disease (AD) is common, distressing, difficult to treat, and inadequately understood. It occurs more frequently in AD than in older adults without dementia. The reasons why some patients develop depression during AD and others do not remain obscure. Objective: We aimed to characterize depression in AD and to identify risk factors. Methods: = 757 with AD). Depression ratings were available using the GDS and NPI and in addition for BDR the Cornell. A cut-off of≥8 was used for the GDS and the Cornell Scale for Depression in Dementia,≥6 for the NPI depression sub-scale, and≥2 for the NPI-Q depression sub-scale. We used logistic regression to examine potential risk factors and random effects meta-analysis and an interaction term to look for interactions between each risk factor and the presence of cognitive impairment. Results: In individual studies there was no evidence of a difference in risk factors for depressive symptoms in AD. In the meta-analysis the only risk factor which increased the risk of depressive symptoms in AD was previous depression, but information on this was only available from one study (OR 7.78 95% CI 4.03-15.03). Conclusion: Risk factors for depression in AD appear to differ to those for depression per se supporting suggestions of a different pathological process, although a past history of depression was the strongest individual risk factor.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".