Comparison between transperineal ultrasound and digital detection of levator ani trauma. Can we improve the odds?
Bibliographic record
Abstract
AIMS: To investigate the predictive ability of four digital assessment parameters to detect levator ani (LA) muscle defects (avulsion injury) and compare these to transperineal tomographic ultrasound images. METHODS: This was an observational study imbedded in a larger quasi-experimental cohort study for women with urinary incontinence. Seventy-two women, ≥ 60 years who had attended or were going to attend physiotherapy for treatment of urinary incontinence, were included in the study. Inclusion criteria from the parent study were symptoms of stress, urge or both types of urinary incontinence. The predictive ability of the following digital parameters: direct palpation of a discontinuity of the LA muscle from insertion on the pubic ramus; palpation of the distance between the muscle insertion sites; palpation of LA strength; palpation of LA tone, were analyzed against findings from tomographic transperineal ultrasound images. Correlation between methods was measured using Cohen's kappa for each of the individual parameters. RESULTS: Seventeen women (24%) presented with a complete or partial avulsion of the puborectalis muscle as diagnosed with tomographic ultrasound imaging. Nine women (13%) had complete avulsions, one of which was bilateral. The predictive ability of the digital assessment parameters varied from poor (k = 0.187, 95% CI [0.02-0.36]) to moderate (k = 0.569, 95% CI [0.31-0.83]). The new parameter of 'width between insertion sites' performed best. CONCLUSIONS: Adding the parameter of "width between insertion sites" appears to enhance our ability to detect avulsion of the levator ani (LA) muscle by digital examination however it does not distinguish between unilateral or bilateral avulsion.
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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 | 0.028 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".