Using 3D transperineal ultrasound imaging to study pelvic floor muscle functional characteristics in women with and without provoked vestibulodynia
Bibliographic record
Abstract
Provoked vestibulodynia (PVD) is the most common form of chronic vulvar pain and cause of painful sexual intercourse in pre-menopausal women. Pelvic floor muscle (PFM) dysfunction is thought to play a role in the development and/or maintenance of PVD, and physical therapy is considered the best treatment approach to target PFM dysfunction. The aims of this thesis work were (i) to use three-dimensional (3D) transperineal ultrasound imaging (TPUSI) to study PFM morphology and function in women with and without PVD in order to better understand how the PFMs may be involved in PVD and (ii) to understand the relevance of using 3D TPUSI in comparison with intra-vaginal palpation, for PFM assessment in women. Three studies were conducted. The first study established that there is very good inter-examiner reliability when using 3D TPUSI for measuring levator hiatal dimensions at rest, during maximal voluntary contraction (MVC) of the PFMs, and during maximal Valsalva maneuver (MVM), when trained examiners perform independent image acquisition and analyses. The second study compared levator hiatal dimensions at rest, on MVC, and on MVM, and transient changes in dimensions from rest to MVC and from rest to MVM, between women with and without PVD. Results revealed that women with PVD had smaller levator hiatus’ and less hiatal enlargement on MVM, in comparison to controls. Results also suggested that levator hiatal dimensions at rest may have an influence on the transient changes in dimensions on MVC and on MVM. The third study investigated the relationships among levator hiatal dimensions assessed using 3D TPUSI and PFM outcomes assessed using digital intra-vaginal palpation. Results revealed significant weak to moderate correlations between ultrasound and palpation outcomes, highlighting that 3D TPUSI and intra-vaginal palpation provide related but distinct information. This thesis provides new information on the utility of 3D TPUSI for PFM assessment in women and on the differences in PFM morphological and functional characteristics between women with and without PVD. Future work may extend these findings to study the effects of physical therapy in the treatment of PVD using 3D TPUSI to determine if improvements in symptoms are associated with post-treatment changes in PFM morphological and functional characteristics.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".