Antidepressants during and after Menopausal Transition: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
To assess the therapeutic benefits of antidepressants in depressive women during and after menopausal transition, PubMed, Cochrane Library, EMBASE and Science Direct were systematically searched from inception to February 1, 2020 for randomized controlled trials examining antidepressants compared to placebo. Primary outcome was change in depressive symptom severity, while secondary outcomes were rates of response/remission rates and dropout/discontinuation due to adverse events. Seven trials involving 1,676 participants (mean age = 52.6 years) showed significant improvement in depressive symptoms (k = 7, Hedges' g = 0.44, 95% confidence interval (CI) = 0.32 to 0.57, p < 0.001) relative to that in controls. Furthermore, response (k = 3, odds ratio (OR) = 2.53, 95% CI = 1.24 to 5.15, p = 0.01) and remission (k = 3, OR = 1.84, 95% CI = 1.32 to 2.57, p < 0.001) rates were significantly higher in antidepressant-treated groups compared to those with controls. Although dropout rates did not differ between antidepressant and control groups (k = 6, OR = 0.93, 95% CI = 0.70 to 1.26, p = 0.68), the rate of discontinuation due to adverse events was significantly higher in antidepressant-treated groups (k = 6, OR = 0.55, 95% CI = 0.35 to 0.86, p = 0.01). Subgroup analysis indicated that antidepressants were also efficacious for depressive symptoms in those without diagnosis of MDD. The results demonstrated that antidepressants were efficacious for women with depressive syndromes during and after menopausal transition but associated with a higher risk of discontinuation due to adverse events.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.025 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".