Lumbar multifidus characteristics and body composition in university level athletes
Bibliographic record
Abstract
Introduction Body composition is well known to affect sport performance and previous studies suggested that structural and functional lumbar multifidus (LM) impairments in athletes were associated with low back pain and lower leg injuries. However few studies have examined the relationship between LM characteristics and body composition in athletic populations. Methods This cross-sectional study included a total of 134 university varsity athletes (hockey, soccer, rugby, and football players). Ultrasound imaging was used to examined LM characteristics at the L5 bilaterally [e.g., size, thickness at rest, thickness during contraction, echo-intensity (EI) and % thickness change from rest to contraction] and body composition parameters (dual-energy x-ray absorptiometry). Pearson correlations were used to assess the relationship between LM characteristics and body composition parameters. One-way ANOVA assessed differences in LM characteristics and body composition between sports. All analyses were performed separately by sex. Results LM size and thickness were positively correlated with weight, height, lean body mass and total bone mass (male: r = 0.23–0.55, p < 0.01–0.05; female: r = 0.30–0.39, p < 0.01–0.05). LM EI was strongly correlated with % body fat (male: r = 0.62, female: r = 0.71, p < 0.01). LM thickness at rest (r = 0.42, p < 0.01) and contracted (r = 0.27, p < 0.05) were positively correlated and % thickness change was negatively correlated with % body fat in male athletes (r = −0.43, p < 0.01). Discussion The significant differences in body composition and LM characteristics between sports may be attributed to sport specific demands. Understanding connections between body composition and LM may aid in preseason screening for athletes at risk of low back pain or lower leg injuries during the season.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".