Selective Serotonin Reuptake Inhibitors for the Prevention of Post-Stroke Depression: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
There are controversial data on the efficacy and safety profile of selective serotonin reuptake inhibitors (SSRIs) to prevent post-stroke depression (PSD). We performed a systematic search in MEDLINE and SCOPUS databases to identify randomized-controlled trials questioning the use of early SSRI therapy in the post-stroke population and its effect on PSD incidence. We included 6 studies with 6560 participants. We extracted the data on PSD occurrence in association with the treatment arm (SSRI versus placebo), as reported by each study. For safety analysis, we extracted the information on adverse events. A random-effects model was used to calculate the pooled relative risk estimates. Early SSRI therapy was associated with a significant reduction of PSD occurrence compared to placebo (10.4% versus 13.8%; relative risk: 0.75 [95% CI, 0.66-0.86]; absolute risk reduction: 3.4%). SSRI therapy increases the risk of bone fracture (RR 2.28 [95% CI, 1.58-3.30]) and nausea (RR 2.05 [95% CI, 1.10-3.82]) in the post-stroke population. Considering the risk-benefit ratio of early SSRI therapy in the post-stroke population, future research should identify high-risk patients for PSD to improve the risk-benefit consideration of this therapy for use in clinical practice.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.030 |
| Bibliometrics | 0.007 | 0.007 |
| 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) | 0.004 | 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".