Reliability and validity of a mobile home pelvic floor muscle trainer: The Elvie Trainer
Bibliographic record
Abstract
AIMS: Reliability and validity of force measurement and task detection by the Elvie Trainer were evaluated against an intravaginal dynamometer (IVD) and ultrasound (US) imaging. METHODS: Women were recruited from local physiotherapy clinics. At the first visit, pelvic floor muscle (PFM) strength and tone were assessed manually. Women performed two sets of three repetitions of rest, PFM maximal voluntary contraction (MVC), and maximal Valsalva maneuver (MVM) tasks in supine and standing, with the Elvie Trainer in situ. Women performed another set of rest and MVC repetitions with a custom IVD in situ. At the second visit, PFM strength and tone were reassessed manually. Women performed two sets of three repetitions of the rest, PFM MVC, and MVM tasks in supine and standing, with the Elvie Trainer in situ. Concurrent US imaging was then acquired during a final set of PFM MVC and MVM repetitions in supine and standing, while the Elvie Trainer remained in situ. Reliability was evaluated using intraclass correlation coefficients. Validity was evaluated using Spearman's/Pearson's correlations and receiver operator characteristic curves. RESULTS: Thirty women participated in the study. The Elvie Trainer MVC force outcomes exhibited excellent within-day and good between-day reliability, but were significantly lower than IVD measures, and exhibited poor relationships with IVD force outcomes. The Elvie Trainer was able to specify correct/incorrect performance of a PFM MVC. CONCLUSIONS: The Elvie Trainer exhibits acceptable within-day and between-day reliability and can detect the correct performance of PFM MVCs; however, force measurements are not valid indicators of PFM strength and should not be used to measure 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 | 0.005 | 0.016 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".