Regional activation within the vastus medialis in stimulated and voluntary contractions
Bibliographic record
Abstract
This study examined the contribution of muscle fiber orientation at different knee angles to regional activation identified with high-density surface electromyography (HDsEMG). Monopolar HDsEMG signals were collected using a grid of 13 × 5 electrodes placed over the vastus medialis (VM). Intramuscular electrical stimulation was used to selectively activate two regions within VM. The distribution of EMG responses to stimulation was obtained by calculating the amplitude of the compound action potential for each channel; the position of the peak amplitude was tracked across knee angles to describe shifts of the active muscle regions under the electrodes. In a separate experiment, regional activation was investigated in 10 knee flexion-extension movements against a fixed resistance. Intramuscular stimulation of different VM regions resulted in clear differences in amplitude distribution along the columns of the electrode grid (P < 0.001); changes in knee angle resulted in consistent shifts along the rows (P < 0.01) and negligible shifts along the columns of the electrode grid. Regional VM activation was identified in dynamic movement, with distal shifts of the EMG distribution in the eccentric phase of the movement (P < 0.05) and at more flexed knee angles (P < 0.05). HDsEMG was used to describe regional activation across the VM that was not attributable to anatomic factors. Changes in muscle fiber orientation associated with knee joint angle mainly influence the amplitude distribution along the fiber direction. Future studies are needed to understand possible functional roles for regional activation within the VM in dynamic tasks.
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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".