Bibliographic record
Abstract
BACKGROUND: Depressive syndromes in dementia are common, treatment is challenging and controlled intervention studies are small in number. The goal of this paper is to review known information about the etiology, epidemiology and treatment of these syndromes, as summarized at the recent Canadian Consensus Conference on Dementia. METHODS: A number of Medline searches were performed (most recently updated in October 2000) using the subject categories dementia and depression, or apathy or emotional lability and other relevant articles were also reviewed. The background article was edited and amended at the Consensus Conference on Dementia. Final recommendations appearing in the summary article by Patterson et al were accepted by the group consensus process. Clinical discussion and informational updates were added for the current text by the authors. RESULTS: Depressive syndromes, ranging in severity from isolated symptoms to full depressive disorders, increase in dementia. While clear-cut depressive disorder is increased in this population, sub-syndromal disorders are even more common and cause considerable distress. Antidepressant treatment may improve the quality of life in depressed, demented people, although it is less successful than in those without cognitive impairment and carries more risk of iatrogenic effects. CONCLUSIONS: Physicians should be alert to the presence of depressive syndromes in dementia. Depressive illness should be treated and, when necessary, referral should be made to an appropriate specialist. Treatment must minimize iatrogenic effects. Although there is some support for treatment of syndromes that do not meet criteria for depressive disorder or dysthymia, the first line of intervention in these situations should involve nonpharmacological approaches.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".