Transdermal oestradiol and testosterone therapy for menopausal depression and mood symptoms: retrospective cohort study
Bibliographic record
Abstract
Background Psychological symptoms in perimenopause and early menopause are common. The impact of menopausal hormone therapy (MHT) on menopausal mood symptoms is unclear. Aims To assess the impact of 17β-oestradiol ± micronised progesterone or the levonorgestrel-releasing intrauterine device, and/or transdermal testosterone, on depressive and anxiety symptoms in peri- and postmenopausal women. Method A real-world retrospective cohort study set in the largest specialist menopause clinic in the UK. The Meno-D questionnaire measured mood-related symptoms. Results The study included 920 women: 448 (48.7%) perimenopausal, and 435 (47.3%) postmenopausal. Following initiation/optimisation of MHT, mean Meno-D scores decreased by 44.59% (95% CI −46.83% to −42.34%, P < 0.001) after average 107 days follow-up. Mood symptoms significantly improved ( P < 0.01 per symptom). Improvement occurred in peri- and postmenopausal women. All MHT regimens improved mental health including both progestogen types (body-identical progesterone and levonorgestrel-releasing intrauterine device), MHT initiation strategy (oestradiol ± a progestogen versus oestradiol ± a progestogen and testosterone, 45.38 v . 48.53%, respectively, P = 0.47) and MHT optimisation strategy (MHT users treated with a higher oestradiol dose versus testosterone added versus both a higher oestradiol dose and testosterone, 34.70, 43.93 and 43.25%, respectively, P = 0.38). Conclusions Use of menopausal hormone therapy was associated with significant improvement in mood in peri- and postmenopausal women. Prospective studies and randomised clinical trials are needed to assess the effects of different regimens in different patient populations over longer time periods.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".