Heterocyclics and Selective Serotonin Reuptake Inhibitors in the Treatment and Prevention of Poststroke Depression
Bibliographic record
Abstract
OBJECTIVES: To review the best current evidence on heterocyclic and serotonin-reuptake inhibitor (SSRI) treatments of poststroke depression (PSD). DESIGN: A literature review using multiple databases was conducted to identify randomized, controlled trials of the treatment or prevention of PSD. Odds ratios were used to test for significant treatment response between the treatment arms for dichotomous outcomes. Continuous outcome measures were evaluated using weighted mean difference and 95% confidence intervals. SETTING: Literature review. PARTICIPANTS: Patients with stroke enrolled in the study of PSD of each selected article. MEASUREMENTS: Frequency of patients with and without depression; frequency of patients who responded to treatment. RESULTS: Nine articles were reviewed. Six investigated use of antidepressant therapy on treatment of PSD, and three examined the prevention of PSD. There was evidence to suggest that patients responded to treatment with antidepressants and significantly improved on depression scales, but treatment, especially with heterocyclic antidepressants, led to a significant number of dropouts due to side effects. There were insufficient data to pool the results of the prevention-based studies. CONCLUSION: Treatment with heterocyclic antidepressants and SSRIs appears to be a viable option for PSD, but their absolute or relative efficacy has yet to be fully established. The effectiveness of early initiation of antidepressants in the prevention of PSD is not clear.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".