Cognitive Outcomes After Antidepressant Pharmacotherapy for Late-Life Depression: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: The authors evaluated whether treatment of late-life depression (LLD) with antidepressants leads to changes in cognitive function. METHODS: A systematic review and meta-analysis of prospective studies of antidepressant pharmacotherapy for adults age 50 or older (or mean age of 65 or older) with LLD was conducted. MEDLINE, EMBASE, and PsycInfo were searched through December 31, 2022. The primary outcome was a change on cognitive test scores from baseline to after treatment. Secondary outcomes included the effects of specific medications and the associations between changes in depressive symptoms and cognitive test scores. Participants with bipolar disorder, psychotic depression, dementia, or neurological disease were excluded. Findings from all eligible studies were synthesized at a descriptive level, and a random-effects model was used to pool the results for meta-analysis. RESULTS: Twenty-two studies were included. Thirteen of 19 studies showed an improvement on at least one cognitive test after antidepressant pharmacotherapy, with the most robust evidence for the memory and learning (nine of 16 studies) and processing speed (seven of 10 studies) domains and for sertraline (all five studies). Improvements in depressive symptoms were associated with improvement in cognitive test scores in six of seven relevant studies. The meta-analysis (eight studies; N=493) revealed a statistically significant overall improvement in memory and learning (five studies: effect size=0.254, 95% CI=0.103-0.404, SE=0.077); no statistically significant changes were seen in other cognitive domains. The evaluated risk of publication bias was low. CONCLUSION: Antidepressant pharmacotherapy of LLD appears to improve certain domains of cognitive function, particularly memory and learning. This effect may be mediated by an improvement in depressive symptoms. Studies comparing individuals receiving pharmacotherapy with untreated control participants are needed.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.039 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".