The influence of hip mobility and quadriceps fatigue on sagittal spinal posture and muscle activation in rugby scrum performance
Bibliographic record
Abstract
Rugby Union scrumming puts the spine under a high degree of loading. The aim of the current study was to determine how sagittal hip range of motion and quadriceps fatigue influence force output, spinal posture, and activation of the trunk and quadriceps muscles in rugby scrumming. Measures of sagittal hip flexion/extension range of motion were collected from 16 male varsity and club first XV level participants. Sagittal spine motion (electromagnetic motion capture), trunk and quadriceps activation (electromyography), and applied horizontal compressive force (force plate) were measured during individual machine scrumming. Participants performed a 5-trial scrum block involving 5 s of contact with 1-2 min recovery between each trial. They then performed a fatiguing protocol (wall sit to failure) and immediately returned to the scrum machine to perform another five trials. Though there was no significant influence of fatigue on the horizontal compressive force applied during contact (P = .83), there was a 52% increase in cervical flexion (P < .001), as well as decreased (18-23% lower) abdominal and erector spinae muscle activation (P < .05). Furthermore, quadriceps activation decreased (12-25% lower) over the course of the initial scrum block but increased (13-15% higher) in the post fatigue block (P < .05). Although athletes were able to maintain force output following the fatiguing wall sit task, there is the potential that they may be at an increased risk of spinal injury due to the combination of increased flexion and high-applied compressive force; a combination which has been shown to increase the likelihood of intervertebral disc herniation.
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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 | 0.000 | 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) | 0.001 | 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".