Associations between low back pain, urinary incontinence, and abdominal muscle recruitment as assessed via ultrasonography in the elderly
Bibliographic record
Abstract
BACKGROUND: Low back pain (LBP) and urinary incontinence (UI) are highly prevalent among elderly individuals. In young adults, changes in trunk muscle recruitment, as assessed via ultrasound imaging, may be associated with lumbar spine stability. OBJECTIVE: To assess the associations between LBP, UI, and the pattern of transversus abdominis (TrA), internal (IO), and external oblique (EO) muscle recruitment in the elderly as evaluated by ultrasound imaging. METHOD: Fifty-four elderly individuals (mean age: 72±5.2 years) who complained of LBP and/or UI as assessed by the McGill Pain Questionnaire, Incontinence Questionnaire-Short Form, and ultrasound imaging were included in the study. The statistical analysis comprised a multiple linear regression model, and a p-value <0.05 was considered significant. RESULTS: The regression models for the TrA, IO, and EO muscle thickness levels explained 2.0% (R2=0.02; F=0.47; p=0.628), 10.6% (R2=0.106; F=3.03; p=0.057), and 10.1% (R2=0.101; F=2.70; p=0.077) of the variability, respectively. None of the regression models developed for the abdominal muscles exhibited statistical significance. A significant and negative association (p=0.018; β=-0.0343) was observed only between UI and IO recruitment. CONCLUSION: These results suggest that age-related factors may have interfered with the findings of the study, thus emphasizing the need to perform ultrasound imaging-based studies to measure abdominal muscle recruitment in the elderly.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".