Does menopause hormone therapy improve symptoms of depression? Findings from a specialized menopause clinic
Bibliographic record
Abstract
OBJECTIVE: Depressive symptoms are commonly reported during the perimenopause and in the early postmenopausal years. Although menopausal hormone therapy (MHT) is considered the most effective treatment option for vasomotor symptoms, its effect on mood-related symptoms is less established. This study aims to assess interval change in depressive symptoms after initiation of MHT treatment in women seeking care at a Canadian specialized menopause clinic. METHODS: Women and female-presenting people attending the St. Joseph's Healthcare Menopause Clinic in Hamilton, Ontario, were invited to participate in this study. Participants (n = 170) completed a self-report questionnaire, which included their medical history as well as validated tools for bothersome symptoms at their initial visit. A shortened version was administered at the follow-up visit 3 to 12 months later with the same validated tools. We sought to examine interval changes on the Center for Epidemiological Studies Depression Scale based on type of treatment used and MHT dose, while controlling for relevant demographic variables (smoking, education level, age). RESULTS: There was a high rate of depressive symptoms in those seeking specialized menopause care (62%). MHT use was associated with significantly improved depressive symptoms, both alone and in addition to an antidepressant medication ( P < 0.001). Younger age, lower education attainment, and smoking were all associated with higher depression scores. CONCLUSION: This study supports the use of MHT to improve depressive symptoms experienced by those seeking specialized menopause care. Further investigation into timing of treatment initiation may facilitate a personalized treatment approach to improve quality of life of women in the peri- and postmenopausal years.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".