Predictors of Pelvic Floor Muscle Dysfunction Among Women With Lumbopelvic Pain
Bibliographic record
Abstract
BACKGROUND: There is evidence to suggest that a large proportion of individuals seeking care for lumbopelvic pain also have pelvic floor muscle dysfunction (PFMD). Because the majority of physical therapists do not have the requisite training to adequately assess pelvic floor musculature, determining predictors of PFMD could be clinically useful. OBJECTIVE: The objective was to establish a combination of factors (self-report and physical) predictive of PFMD in women with lumbopelvic pain. DESIGN: This was a cross-sectional study. METHODS: Participants completed a battery of self-report and physical assessments (masked assessors). Three clinical findings characterized PFMD: weakness of the pelvic floor, lack of coordination of the pelvic floor, and pelvic floor muscle tenderness on palpation (bilateral obturator internus). Univariate and multivariate logistic regression analyses were used to determine the extent to which different predictors were associated with PFMD. RESULTS: One hundred eight women with self-reported lumbopelvic pain (within the past week) were included in the study (mean age = 40.4 years; SD = 12.6 years). None of the examined factors predicted pelvic floor muscle weakness. Two factors independently predicted pelvic floor muscle tenderness on palpation: very strong and/or uncontrollable urinary urges (odds ratio [OR] = 2.93; 95% confidence interval [CI] = 1.13-7.59) and Central Sensitization Inventory scores of 40 or greater (OR = 3.13; 95% CI = 1.08-9.10). LIMITATIONS: The sample consisted of young women, some of whom were not actively seeking care. Additionally, the technique for assessing pelvic floor muscle tenderness on palpation requires further validation. CONCLUSIONS: Women who have lumbopelvic pain, uncontrollable urinary urgency, and central sensitization were, on average, 2 times more likely to test positive for pelvic floor muscle tenderness on palpation. Further studies are needed to validate and extend these findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".