Speed and force validation of an improved intravaginal dynamometer design
Bibliographic record
Abstract
Intravaginal dynamometry can provide reliable and objective assessment of the active and passive properties of the female pelvic floor muscles (PFMs) and associated connective tissues. This work presents a new automated intravaginal dynamometer (IVD) designed to address the limitations of many devices described in the literature, and provides a preliminary mechanical characterization and validation of the system. The new IVD includes dual (anterior and posterior) force measurement probes, minimalistic actuators to reduce IVD size and weight, off-the-shelf components optimized for cost and performance, integrated concurrent electromyography recordings, and an easy-to-use graphic user interface (GUI). IVD load measurements were validated against an Instron® Universal Tester (0-28N) and probe opening speeds were validated using video analysis. A linear regression model was used to quantify the input/output relationship in both cases (α=0.05). While the IVD exhibited -0.828 N bias in load measurements, there was a definitive linear relationship between IVD and Instron® force measurement, with a slope of 0.950 and an excellent model fit (adjR2=1.000). The linear relationships between the GUI set speed of arm opening and true speed measured by video analysis were also excellent (0.958<adjR2<0.991), slopes ranged from 0.874-0.980. The bias and the standard deviation of the bias of speeds ranged from -3.987mm/s to -0.809mm/s and 2.817mm/s to 1.207mm/s, respectively, generally decreasing in magnitude with increasing diameters. While fit was still excellent, speed of opening exhibited lower validity (i.e. lower slopes) at smaller apertures, which may be due to inertia effects. The IVD design presented here demonstrates valid force and speed values during bench testing.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".