Management of sexual dysfunction in menopause: an update on evidence-based strategies
Bibliographic record
Abstract
INTRODUCTION: Female sexual dysfunction (FSD) at menopause is a group of prevalent and multifactorial conditions that significantly impair quality of life and well-being. Hormonal decline, particularly estrogen and androgen deprivation, along with aging-related changes, contribute to genitourinary syndrome of menopause (GSM) and hypoactive sexual desire disorder (HSDD), the two most relevant clinical entities. AREAS COVERED: This narrative review provides an updated revision of the literature regarding evidence-based strategies for FSD in menopausal women. Emphasis is placed on pharmacologic treatments, including systemic and local hormonal and non-hormonal therapies, selective estrogen receptor modulators (SERM), and centrally acting agents. Non-pharmacological interventions are also discussed, such as pelvic floor rehabilitation, laser therapies, and psychosexual approaches (cognitive behavioral therapy, mindfulness, couple therapy). The content draws mainly on recent clinical trials, consensus statements, and guideline-based recommendations, based on Pubmed search updated to July 2025. EXPERT OPINION: Management of FSD associated with menopause requires a multidimensional, individualized approach integrating biological, psychological, and relational factors. A proactive and structured clinical framework, supported by validated diagnostic tools, is essential. Healthcare providers (HCPs) should address sexual health openly, recognize the couple's dynamics, and tailor interventions to each woman's needs and preferences, while considering the evolving evidence and regulatory landscape.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".