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Record W2073179067 · doi:10.1016/j.rehab.2014.05.008

Sexual disorders in women with MS: Assessment and management

2014· review· en· W2073179067 on OpenAlexaff
Dany Cordeau, F. Courtois

Bibliographic record

VenueAnnals of Physical and Rehabilitation Medicine · 2014
Typereview
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsPhysical medicine and rehabilitationPhysical therapyMedicinePsychology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.929
Threshold uncertainty score0.570

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.064
GPT teacher head0.440
Teacher spread0.376 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations71
Published2014
Admission routes1
Has abstractyes

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