Escitalopram versus ethinyl estradiol and norethindrone acetate for symptomatic peri- and postmenopausal women
Bibliographic record
Abstract
OBJECTIVE: To examine the efficacy and tolerability of escitalopram (ESCIT) compared to estrogen and progestogen therapy (EPT) for the treatment of symptomatic peri- and postmenopausal women. DESIGN: Forty women (aged 40-60 years) with depressive disorders and menopause-related symptoms were randomly assigned to an 8-week open trial with ESCIT (flexible dose, 10-20 mg/day; fixed dose, 10 mg/day for the first 4 weeks) or estrogen plus progestogen therapy (ethinyl estradiol 5 microg/day plus norethindrone acetate 1 mg/day). Primary outcome measures included Montgomery-Asberg Depression Rating Scale and the Greene Climacteric Scale at week 8. Secondary outcome measures included the Clinical Global Impressions as well as sleep and quality of life assessments. RESULTS: Thirty-two women (16 on EPT, 16 on ESCIT) were included in the analyses. Full remission of depression (score of <10 on the Montgomery-Asberg Depression Rating Scale) was observed in 75% (12/16) of subjects treated with ESCIT, compared to 25% (4/16) treated with EPT (P = 0.01, Fisher's exact tests). Remission of menopause-related symptoms (>50% decrease in Greene Climacteric Scale scores) was noted in 56% (9/16) of women treated with ESCIT compared to 31.2% (5/16) on EPT (P = 0.03, Pearson's chi2 tests). Improvement in sleep, hot flashes, and quality of life was observed with both treatments. CONCLUSIONS: ESCIT is more efficacious than EPT for the treatment of depression and has a positive impact on other menopause-related symptoms. ESCIT may constitute a treatment option for symptomatic menopausal women who are unable or unwilling to use hormone therapy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".