Sexual disorders in women with MS: Assessment and management
Bibliographic record
Abstract
OBJECTIVES: Summarize the data on sexual disorders in women with multiple sclerosis (MS). METHOD: Review of 99 Pubmed articles covering sexual dysfunction in women with MS. RESULTS: Prevalence of dysfunction in women with MS varies from 34% to 85%. They include poor vaginal lubrication, poor clitoral erection, and anorgasmia, which correlate with level of disability. Specific brain stem and pyramidal lesions appear to correlate with anorgasmia. Age and duration of the disease correlate with sexual disorders, but not age at onset. Secondary consequences of MS, including bladder and bowel dysfunction, spasticity, pain, fatigue, depression, anxiety, and side effects of medication contribute to sexual dysfunction. Treatments can involve alpha-blockers or phosphodiesterase-5 inhibitors to increase smooth muscle relaxation, while lubricants and oestrogen therapy can help vaginal dryness, burning and dyspareunia. Antidepressants can delay (or abolish) orgasm, suggesting reducing dosage or combining them with PDE5 inhibitors. Counselling should emphasize planning sexual activities, reducing fatigue, managing positions, preventing incontinence, promoting sexual aids, extra-genital and other sexual options to achieve pleasurable and intimacy. Psychosocial interventions should include couples' relationship and communication skills to increase satisfaction. CONCLUSION: Sexual dysfunctions in women with MS are amenable to treatments covering primary, secondary and tertiary consequences of the disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".