The state of pelvic floor muscle dynamometry: A scoping review
Bibliographic record
Abstract
AIMS: To discuss the advantages and limitation of the different pelvic floor muscle (PFM) dynamometers available, both in research and industry, and to present the extent of variation between them in terms of structure, functioning, psychometric properties, and assessment procedures. METHODS: We identified relevant studies from four databases (MEDLINE, Compendex, Web of Science, and Derwent Innovations Index) up to December 2020 using terms related to dynamometry and PFM. In addition, we conducted a hand search of the bibliographies of all relevant reports. Peer-reviewed papers, conference proceedings, patents and user's manuals for commercial dynamometers were included and assessed by two independent reviewers. RESULTS: One hundred and one records were included and 23 PFM dynamometers from 15 research groups were identified. From these, 20 were considered as clinical dynamometers (meant for research settings) and three as personal dynamometers (developed by the industry). Overall, significant heterogeneity was found in their structure and functioning, which limits development of normative data for PFM force in women. Further research is needed to assess the psychometric properties of PFM dynamometers and to standardize assessment procedures. CONCLUSION: This review points up to the heterogeneity of existing dynamometers and methods of assessing PFM function. It highlights the need to better document their design and assessment protocol methods. Additionally, this review recommends standards for new dynamometers to allow the establishment of normalized data.
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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 | 0.017 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.029 | 0.027 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".