Interactive effect of acute pain and motor learning acquisition on sensorimotor integration and motor learning outcomes
Bibliographic record
Abstract
Previous work has demonstrated differential changes in early somatosensory evoked potentials (SEPs) when motor learning acquisition occurred in the presence of acute pain; however, the learning task was insufficiently complex to determine how these underlying neurophysiological differences impacted learning acquisition and retention. To address this limitation, we have utilized a complex motor task in conjunction with SEPs. Two groups of 12 participants (n = 24) were randomly assigned to either a capsaicin (capsaicin cream) or a control (inert lotion) group. SEP amplitudes were collected at baseline, after application, and after motor learning acquisition. Participants performed a motor acquisition task followed by a pain-free retention task within 24-48 h. After motor learning acquisition, the amplitude of the N20 SEP peak significantly increased (P < 0.05) and the N24 SEP peak significantly decreased (P < 0.001) for the control group while the N18 SEP peak significantly decreased (P < 0.01) for the capsaicin group. The N30 SEP peak was significantly increased (P < 0.001) after motor learning acquisition for both groups. The P25 SEP peak decreased significantly (P < 0.05) after the application of capsaicin cream. Both groups improved in accuracy after motor learning acquisition (P < 0.001). The capsaicin group outperformed the control group before motor learning acquisition (P < 0.05) and after motor learning acquisition (P < 0.05) and approached significance at retention (P = 0.06). Improved motor learning in the presence of capsaicin provides support for the enhancement of motor learning while in acute pain. In addition, the changes in SEP peak amplitudes suggest that early SEP changes reflect neurophysiological alterations accompanying both motor learning and mild acute pain.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".