Lumbar Multifidus Muscle Characteristics, Body Composition, and Injury in University Rugby Players
Bibliographic record
Abstract
CONTEXT: A smaller lumbar multifidus (LM) muscle was reported to be a strong predictor of lower limb injury in professional Australian Football League players. However, despite the high prevalence of low back pain (LBP) and lower limb injury in rugby players, their LM characteristics have yet to be explored. OBJECTIVE: To (1) examine LM characteristics in male and female university rugby players and their possible associations with LBP and lower limb injury and (2) investigate the relationship between LM characteristics and body composition in this group of athletes. DESIGN: Cross-sectional study. SETTING: University research center. PATIENTS OR OTHER PARTICIPANTS: Thirty-four university rugby players (20 women, 14 men). MAIN OUTCOME MEASURE(S): Ultrasound measurements of LM cross-sectional area (CSA), thickness, and percentage change in thickness during contraction were obtained bilaterally, at the L5-S1 level, in prone and standing positions. Body composition measures were obtained using dual-energy x-ray absorptiometry. Self-reported questionnaires were used to obtain LBP and lower limb injury history. RESULTS: Players who reported LBP in the previous 3 months showed a smaller percentage change in thickness during contraction in the standing position (F = 5.21, P = .03). The LM CSA side-to-side asymmetry (right versus left) was greater in players who reported having a lower limb injury in the previous 12 months (F = 4.98, P = .03). The LM CSA was significantly associated with body composition measurements. A greater percentage change in thickness during contraction was significantly associated with a lower percentage of body fat. The LM echo intensity was strongly associated with the total percentage of body fat and was significantly greater in women. CONCLUSIONS: The influence of body composition on LM morphology in athletes cannot be ignored and warrants further investigation. Our findings also provide preliminary evidence of an association among LM morphology, LBP, and lower limb injury in university rugby players.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".