Muscle Activity Adaptations to Spinal Tissue Creep in the Presence of Muscle Fatigue
Bibliographic record
Abstract
AIM: The aim of this study was to identify adaptations in muscle activity distribution to spinal tissue creep in presence of muscle fatigue. METHODS: Twenty-three healthy participants performed a fatigue task before and after 30 minutes of passive spinal tissue deformation in flexion. Right and left erector spinae activity was recorded using large-arrays surface electromyography (EMG). To characterize muscle activity distribution, dispersion was used. During the fatigue task, EMG amplitude root mean square (RMS), median frequency and dispersion in x- and y-axis were compared before and after spinal creep. RESULTS: Important fatigue-related changes in EMG median frequency were observed during muscle fatigue. Median frequency values showed a significant main creep effect, with lower median frequency values on the left side under the creep condition (p≤0.0001). A significant main creep effect on RMS values was also observed as RMS values were higher after creep deformation on the right side (p = 0.014); a similar tendency, although not significant, was observed on the left side (p = 0.06). A significant creep effects for x-axis dispersion values was observed, with higher dispersion values following the deformation protocol on the left side (p≤0.001). Regarding y-axis dispersion values, a significant creep x fatigue interaction effect was observed on the left side (p = 0.016); a similar tendency, although not significant, was observed on the right side (p = 0.08). CONCLUSION: Combined muscle fatigue and creep deformation of spinal tissues led to changes in muscle activity amplitude, frequency domain and distribution.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".