Regional Distribution of Myoelectric Median Frequency in the Erector Spinae Muscles Under the Influence of Delayed-Onset Muscle Soreness
Bibliographic record
Abstract
Delayed-onset muscle soreness (DOMS) is a noninvasive pain model offering a unique opportunity to study trunk neuromuscular adaptations. While prior research has examined regional muscle activation in the lumbar region, the spatial distribution of median frequencies (MF) under DOMS has not been explored. This study investigated the effect of DOMS-induced pain on the spatial distribution of MF in the lumbar erector spinae muscles and its association with trunk force variability during submaximal contractions. Twenty healthy adults completed 2 laboratory sessions: 1 pain-free and 1 under low back DOMS. High-density surface electromyography was recorded bilaterally on the erector spinae during submaximal isometric trunk extensions. MF distribution was analyzed using centroid coordinates with and without DOMS. Force variability was also assessed. DOMS significantly increased perceived muscle pain and soreness in the lumbar region. It also caused a cranial and medial shift of the MF centroid, significant on 1 side of the trunk. However, force variability remained stable between conditions. These results suggest that DOMS induces regional adaptations in lumbar muscle MF. The spatial distribution of MF may serve as a novel and sensitive marker of neuromuscular adaptation to pain. The trunk system was able to maintain force steadiness despite pain and soreness.
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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".