Effect of Experimental Hand Pain on Training-Induced Changes in Motor Performance and Corticospinal Excitability
Bibliographic record
Abstract
Pain influences plasticity within the sensorimotor system and the aim of this study was to assess the effect of pain on changes in motor performance and corticospinal excitability during training for a novel motor task. A total of 30 subjects were allocated to one of two groups (Pain, NoPain) and performed ten training blocks of a visually-guided isometric pinch task. Each block consisted of 15 force sequences, and subjects modulated the force applied to a transducer in order to reach one of five target forces. Pain was induced by applying capsaicin cream to the thumb. Motor performance was assessed by a skill index that measured shifts in the speed–accuracy trade-off function. Neurophysiological measures were taken from the first dorsal interosseous using transcranial magnetic stimulation. Overall, the Pain group performed better throughout the training (p = 0.03), but both groups showed similar improvements across training blocks (p < 0.001), and there was no significant interaction. Corticospinal excitability in the NoPain group increased halfway through the training, but this was not observed in the Pain group (Time × Group interaction; p = 0.01). These results suggest that, even when pain does not negatively impact on the acquisition of a novel motor task, it can affect training-related changes in corticospinal excitability.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".