Pelvic floor maximal strength using vaginal digital assessment compared to dynamometric measurements
Bibliographic record
Abstract
AIM: To compare vaginal digital assessment with dynamometric measurements for determining the maximal strength of the pelvic floor muscles (PFM). MATERIALS AND METHODS: Eighty-nine women aged between 21 and 44 participated in the study. An experienced physiotherapist evaluated the maximal strength of the PFM of these women using the modified Oxford grading system (six categories, range 0-5) and dynamometric measurements. The mean maximal forces obtained for all women with the instrumented speculum for each category of digital assessment were compared using ANOVAs. Spearman's rho coefficients were calculated to assess the correlation between the dynamometric and the digital assessments. RESULTS: According to their symptoms and pad test results, 30 women were continent and 59 had stress urinary incontinence (SUI). Based on dynamometric measurements, important overlaps were observed between each category of digital assessment. The ANOVAs indicated that force values differ across categories (F = 10.08; P < 0.001), although contrast analyses revealed no differences in the mean maximal forces between adjacent digital-assessment categories (1-2, 2-3, 3-4, 4-5). Mean force values differed significantly only between non-adjacent levels in digital assessment, for example, between 1 and 3; 1 and 4; 1 and 5; 2 and 4; 2 and 5 (P < 0.05). Significant correlations were found between the two measurements with coefficients of r = 0.727, r = 0.450, and r = 0.564 for continent, incontinent, and all women, respectively (P < 0.01). CONCLUSIONS: Even if the dynamometric mean forces of the PFM increased across subsequent categories of digital assessment, the force values between two adjacent categories do not differ. This limitation of digital assessment should be considered by clinicians and researchers when choosing treatment orientation and evaluating treatment outcomes.
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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 | 0.008 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".