Fundamentals of terminology in pelvic floor muscle assessment: A concise reference
Bibliographic record
Abstract
A comprehensive standardization of terminology document of the assessment of female and male pelvic floor muscle (PFM) function and dysfunction was published in 2021. A summary of this terminology document was required to provide an overview of the most commonly-used assessment methods and tools in clinical practice, for ease of use by clinicians in their everyday practice. This summary report contains commonly used terms for symptoms, signs, investigations and diagnoses related to PFM function and dysfunction, extracted from the full, comprehensive standardization of terminology document. This summary report represents a concise document for clinicians in their assessment of PFM function and dysfunction, which may offer a quick reference for the busy clinician. In alignment with the full standardization of terminology document, this summary document is not intended to be a recommendation of assessment methods and tools to use in clinical practice or research; rather it is intended to be a summary reference paper for standardized description and definition of assessment method and interpretation of finding when a particular term is used. Psychometric and clinimetric properties of these terms are eagerly awaited, in order to guide decisions in clinical practice and research of the preferred assessment method(s)/tool(s) to measure a particular PFM property.
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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 | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.025 |
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".