Pelvic Floor Muscle Training With and Without Electrical Stimulation in the Treatment of Lower Urinary Tract Symptoms in Women With Multiple Sclerosis
Bibliographic record
Abstract
PURPOSE: The aim of this study was to evaluate the effect of intravaginal neuromuscular electrical stimulation (NMES) and transcutaneous tibial nerve stimulation (TTNS) on lower urinary tract symptoms (LUTS) and health-related quality of life in women undergoing pelvic floor muscle (PFM) training (PFMT) with multiple sclerosis (MS) and to compare the efficacy of these 2 approaches. DESIGN: Randomized controlled trial. METHODS: Thirty women with MS and LUTS were randomly allocated to 1 of 3 groups and received treatment for 12 weeks. Ten women in group 1 received PFMT with electromyographic (EMG) biofeedback and sham NMES. Ten women in group 2 underwent PFMT with EMG biofeedback and intravaginal NMES, and 10 subjects in group 3 received PFMT with EMG biofeedback and TTNS. Multiple assessments, performed before and after treatment, included a 24-hour pad test, 3-day bladder diary, assessment of PFM function (strength and muscle tone), urodynamic studies, and validated questionnaires including Overactive Bladder Questionnaire (OAB-V8), International Consultation on Incontinence Questionnaire-Short Form (ICIQ-SF), and Qualiveen instrument. RESULTS: All groups showed reductions in pad weight, frequency of urgency and urge urinary incontinence episodes, improvement in all domains of the PFM assessment, and lower scores on the OAB-V8 and ICIQ-SF questionnaires following treatment. Subjects in group 2 achieved significantly greater improvement in PFM tone, flexibility, ability to relax PFMs, and OAB-V8 scores when compared to subjects in groups 1 and 3. CONCLUSION: Results suggest that PFMT alone or in combination with intravaginal NMES or TTNS is effective in the treatment of LUTS in patients with MS. The combination of PFMT and NMES offers some advantage in the reduction of PFM tone and symptoms of overactive bladder.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".