A model identifying characteristics predictive of successful pelvic floor muscle training outcomes among women with stress urinary incontinence
Bibliographic record
Abstract
INTRODUCTION AND HYPOTHESIS: The aim of this study was to prospectively identify aspects of baseline demographic, clinical, and pelvic morphology of women with stress urinary incontinence (SUI) that are predictive of cure with physiotherapist-supervised pelvic floor muscle training (PFMT). METHODS: Women ≥18 years old with SUI were recruited from urogynecology and pelvic health physiotherapy clinics. Participants completed a 3-day bladder diary, the International Consultation on Incontinence Questionnaire Urinary Incontinence Short Form (ICIQ-UI-SF), a standardized pad test, manual assessment of pelvic floor muscle (PFM) strength and tone, and transperineal ultrasound (TPUS) assessment of their urogenital structures at rest while in a supine position and standing, and during contraction, straining, and coughing. Participants attended six physiotherapy sessions over 12 weeks and performed a home PFMT program. The assessment was repeated after the intervention; cure was defined as a dry (≤2 g) pad test. RESULTS: Seventy-seven women aged 50 (±10) years completed the protocol; 38 (49%) were deemed cured. Based on univariate testing, four predictors were entered into a binary logistic regression model: ICIQ-UI-SF, PFM tone, bladder neck (BN) height in a quiet standing position, and BN height during a cough in a standing position. The model was significant (p < 0.001), accurately classifying outcome in 74% of participants. The model, validated through bootstrapping, performed moderately, with the area under the receiver operating characteristic curve = 0.80 (95% CI: 0.69-0.90; p = 0.00), and with 70% sensitivity and 75% specificity. CONCLUSIONS: Women with better bladder support in a standing position and less severe symptoms were most likely to be cured with PFMT. CLINICAL TRIAL REGISTRATION: #NCT01602107.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".