Biofeedback Measures of Pelvic Floor Muscle Contraction, Relaxation, and Resting Tone for Males With and Without Chronic Pelvic Pain: A Scoping Review
Bibliographic record
Abstract
Background: We sought to identify and explore the utility of biofeedback assessments used to characterize pelvic floor muscles (PFMs) in terms of contraction, relaxation, and resting tone and determine if these methods have identified differences between males diagnosed with chronic pelvic pain (CPP) as opposed to healthy controls. Methods: A search strategy was developed with the assistance of a health sciences librarian. Search terms were generated related to key concepts including sex, CPP, and biofeedback. Five electronic databases (PubMed, EMBASE, CINAHL, Medline, and PEDro) were searched for English language articles. This scoping review was completed following Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines, and the protocol was registered with the Open Science Framework. Results: Five eligible studies comparing males with CPP to nonpainful controls were identified. Ultrasound imaging, surface electromyography, and anorectal manometry were used in the studies. Identification of increased pelvic floor muscle resting tone and reduced endurance were noted as the most salient pelvic floor muscle findings. Discussion: Biofeedback use included males with urologic chronic pelvic pain and chronic anorectal pain. PFMs may behave differently in males with CPP compared to nonpainful controls as measured using biofeedback methods including ultrasound imaging, surface electromyography, and anorectal manometry; however, further research is needed to verify the conclusions of the studies done to date. Biofeedback assessment measures can be useful when PFMs are involved in CPP to identify specific muscle dysfunction and clarify treatment targets for physiotherapists.
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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 | 0.016 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.020 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".