Seasonal Changes in Lumbar Multifidus Muscle in University Rugby Players
Bibliographic record
Abstract
PURPOSE: Although smaller lumbar multifidus muscle (LMM) was reported to be a strong predictor of lower limb injury (LLI) in Australian Football League players, LMM morphology has not been investigated in rugby athletes. This study examined seasonal changes in LMM in rugby players and whether LMM characteristics were associated with low back pain (LBP) and LLI. METHODS: Ultrasound examinations of the LMM were acquired in 21 university-level rugby players (12 women, 9 men) at preseason and end-season. LMM cross-sectional area (CSA), thickness at rest, and thickness during submaximal contraction (e.g., contralateral arm lift) measurements in prone and standing were obtained bilaterally at the L5-S1 level. The percent change in LMM thickness during contraction was calculated as follows: [(thicknesscontracted - thicknessrest)/thicknessrest × 100]. Self-reported questionnaires were used to acquire data on LBP and LLI. RESULTS: There was no significant difference in LMM characteristics between preseason and end-season measurements (P > 0.05). Preseason LMM CSA, side-to-side CSA asymmetry, and thickness at rest or during contraction were not associated with LBP or LLI. However, a lower percent thickness change in the standing position was significantly associated with having LBP during the preseason (P = 0.01) and playing season (P = 0.001), as well as LLI during the preseason (P = 0.03). CONCLUSIONS: This study provides preliminary evidence that LMM contractile ability and behavior during functional movement, such as standing, may have important implications for the susceptibility to injury among rugby athletes.
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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 | 0.000 |
| 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".