The impact of a familiarization session on the magnitude and stability of active and passive pelvic floor muscle forces measured through intravaginal dynamometry
Bibliographic record
Abstract
AIMS: The aim of this study was to investigate the impact of task familiarization on (1) the magnitude and (2) the repeatability of active and passive properties of the female pelvic floor muscles (PFMs) measured using automated intra-vaginal dynamometry. METHODS: Women attended three laboratory sessions at one-week intervals. After receiving initial task instruction and feedback at the start of the first session, standardized instructions were given while women performed maximal effort voluntary contractions of their PFMs with the dynamometer arms open at two different diameters and kept their PFMs relaxed while the dynamometer arms opened to 40 mm at two speeds. Outcomes included baseline force, peak force, relative peak forces (N), rate of force development (N/s) and stiffness. Between session effects were tested for all outcomes using one-way ANOVAs. Intra-class correlation coefficients (ICCs) and minimal detectable change values were computed within each session and between sessions 1 and 2 and sessions 2 and 3. RESULTS: Twenty nulliparous women (mean age = 35 ± 15 years) participated. No differences in the mean values were found across the three visits for any outcomes. Within sessions, neither ICC nor minimal detectable change differed among sessions and between-session ICC values were not different between visits 1 and 2 and visits 2 and 3. CONCLUSIONS: There is no evidence of a familiarization effect over a two-week period on the amplitude nor repeatability of dynamometric measures of active or passive PFM properties recorded from nulliparous women.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.004 | 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".