Antidepressants for Depression in Patients with Dementia: A Review of the Literature
Bibliographic record
Abstract
OBJECTIVE: To evaluate the literature investigating the efficacy and safety of antidepressants for treating depression in individuals with dementia. DATA SOURCES: A literature search was conducted using MEDLINE, PUBMED, EMBASE, and Cochrane databases from inception to May 2013 for studies in English that evaluated the treatment of depression in patients with dementia. All relevant randomized controlled trials (RCTs) and meta-analyses were identified using the search terms "dementia" or "Alzheimer's disease," and "depression" or "major depressive disorder." Reference lists from retrieved articles and practice guidelines were also searched for relevant literature. STUDY SELECTION AND DATA EXTRACTION: Only randomized, placebo-controlled trials and meta-analyses that compared an antidepressant with placebo for the treatment of depression in patients with dementia were included. DATA SYNTHESIS: In this systematic review, 10 RCTs and 3 meta-analyses were identified that examined the efficacy and safety of antidepressants compared with placebo in treating depression in patients with dementia. The majority of the RCTs consisted of a small sample size, and the antidepressants studied were not routinely used in practice. CONCLUSIONS: The evidence for antidepressants in the treatment of depression in patients with dementia is inconclusive. The accumulation of evidence suggests nonpharmacologic approaches and watchful waiting be attempted for the first 8 to 12 weeks in a patient who presents with both mild-to-moderate depression and dementia. In cases of severe depression, or depression not managed through nonpharmacologic means, a trial of an antidepressant may be initiated. However, further well-designed trials are needed to support these recommendations.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".