Proximal Upper Limb Sensorimotor Integration in Response to Novel Motor Skill Acquisition
Bibliographic record
Abstract
Previous studies have shown significant changes in cortical and subcortical evoked potential activity levels in response to motor training with the distal upper-limb muscles. However, no studies to date have assessed the neurological processing changes in somatosensory evoked potentials (SEPs) associated with motor training whole-arm movements utilizing proximal upper-limb muscles. The proximal upper-limb muscles are a common source of work-related injuries, due to repetitive glenohumeral movements. Measuring neurophysiological changes following performance of a proximal motor task provide insight into potential neurophysiological changes associated with occupational postures and movements involving proximal upper limb muscles. This study sought to assess the impact of a novel motor skill acquisition task on neural processing of the proximal upper-limb muscle groups, through the measurement of short-latency median nerve SEPs. One group of 12 participants completed a novel motor training task, consisting of tracing a sinusoidal waveform varying in amplitude and frequency. Baseline SEP measurements were recorded from each participant, followed by a mental recitation control task. Pre-test SEP measurements were then recorded, followed by the motor training task, and post-test SEP recordings. The participants completed the tracing with their right thumb, using glenohumeral rotation only to move their hand. Significant improvements in task accuracy were demonstrated, indicating that motor acquisition had occurred. Significant changes were also seen in the N11, N13, N20, N24, P25, and the N30 SEP peaks were seen following the motor training task. Conclusion: Early SEPs appear to be a sensitive measure of changes in sensorimotor integration in response to novel motor skill acquisition within the proximal upper-limb muscles.
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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".