Changes in the flexion-relaxation response induced by hip extensor and erector spinae muscle fatigue
Bibliographic record
Abstract
BACKGROUND: The flexion-relaxation phenomenon (FRP) is defined by reduced lumbar erector spinae (ES) muscle myoelectric activity during full trunk flexion. The objectives of this study were to quantify the effect of hip and back extensor muscle fatigue on FRP parameters and lumbopelvic kinematics. METHODS: Twenty-seven healthy adults performed flexion-extension tasks under 4 different experimental conditions: no fatigue/no load, no fatigue/load, fatigue/no load, and fatigue/load. Total flexion angle corresponding to the onset and cessation of myoelectric silence, hip flexion angle, lumbar flexion angle and maximal trunk flexion angle were compared across different experimental conditions by 2 x 2 (Load x Fatigue) repeated-measures ANOVA. RESULTS: The angle corresponding to the ES onset of myoelectric silence was reduced after the fatigue task, and loading the spine decreased the lumbar contribution to motion compared to the hip during both flexion and extension. A relative increment of lumbar spine motion compared to pelvic motion was also observed in fatigue conditions. CONCLUSIONS: Previous results suggested that ES muscles, in a state of fatigue, are unable to provide sufficient segmental stabilization. The present findings indicate that, changes in lumbar-stabilizing mechanisms in the presence of muscle fatigue seem to be caused by modulation of lumbopelvic kinematics.
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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) | 0.002 | 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".