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Record W4377968829 · doi:10.1093/jsxmed/qdad061.030

(030) Reliability of Shear Wave Elastography for the Assessment of Pelvic Floor Muscle Stiffness

2023· article· en· W4377968829 on OpenAlexaff
Małgorzata Starzec‐Proserpio, María Teresa Soler Roch, Nathalie Gaudreault, Frédérique Daigle, Nathalie J. Bureau, Mélanie Morin

Bibliographic record

VenueThe Journal of Sexual Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicPelvic floor disorders treatments
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire de SherbrookeUniversité de Sherbrooke
Fundersnot available
KeywordsPelvic floorIntraclass correlationMedicineAsymptomaticPhysical therapyReliability (semiconductor)OrthodonticsPhysical medicine and rehabilitationSurgery

Abstract

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Abstract Introduction Shear wave elastography (SWE) has gained popularity for assessing skeletal muscle stiffness and could potentially overcome some limitations associated with the assessment of the pelvic floor muscles (PFMs). Additionally, SWE is a non-invasive and painless alternative to the current method of assessing the PFMs, which is often painful or even impossible to perform in women with sexual pain. Before implementing this promising method, it is of the utmost importance to assess its reliability. Available data are limited and currently, there is no study investigating the reliability of SWE for the assessment of the different PFMs. Objective To investigate the intra-rater and inter-rater reliability of SWE in measuring the stiffness of the different PFMs at rest and during maximal voluntary contraction in asymptomatic women. Methods Thirty asymptomatic nulliparous women participated in the study. They each attended a single assessment session conducted by two independent assessors. SWE assessments were performed using the Aixplorer device (Supersonic Imaging) equipped with linear probes. Four PFMs (i.e., bulbospongiosus, ischiocavernosus, transverse perineal, puborectalis) and the perineal body were assessed at rest and during maximal voluntary contraction. Measurements were taken twice by the first assessor (intra-rater reliability) and once by the second assessor (inter-rater reliability). SWE data were saved and analyzed offline. For both the resting and contraction states, three consecutive frames with stable SWE maps were selected. The average shear modulus was evaluated by manually defining a region of interest that corresponded to the structure assessed. To determine intra- and inter-rater reliability, intraclass correlation coefficients (ICC) were calculated. Results Intra-rater reliability was excellent for the ischiocavernosus and puborectalis at rest and during contraction as well as for the transverse perineal and perineal body during contraction (ICC 0.82-0.95); good for the bulbospongiosus during contraction as well as the transverse perineal and perineal body at rest (ICC 0.61-0.79); and fair for the bulbospongiosus at rest (ICC 0.51). For the inter-rater reliability, the results were as follows: excellent for the transverse perineal and puborectalis during contraction (ICC 0.84-0.89); good for the puborectalis and transverse at rest as well as the perineal body during contraction (ICC 0.62-0.72); fair for the perineal body at rest (ICC 0.48); and poor for the bulbospongiosus and ischiocavernosus muscles (ICC 0.22-0.38) in both states. Conclusions These findings support the intra- and inter-rater reliability of SWE for the assessment of PFM stiffness in women, especially in the puborectalis muscle, transverse perineal muscle, and perineal body. By enabling the assessment of localized areas of stiffness in the different PFMs, SWE may be a stepping stone in enhancing our understanding of the role of the PFMs in the pathophysiology and treatment of sexual pain. Further studies investigating PFM stiffness with the use of SWE assessment are encouraged. Disclosure No

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.042
GPT teacher head0.340
Teacher spread0.298 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations1
Published2023
Admission routes1
Has abstractyes

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