Tonic Pain Experienced during Locomotor Training Impairs Retention Despite Normal Performance during Acquisition
Bibliographic record
Abstract
Many patients are in pain when they receive gait training during rehabilitation. Based on animal studies, it has been proposed that central sensitization associated to nociception (maladaptive plasticity) and plasticity related to the sensorimotor learning (adaptive plasticity) share similar neural mechanisms and compete with each other. The aim of this study was to evaluate whether experimental tonic pain influences motor learning (acquisition and next-day retention) of a new locomotor task. Thirty healthy human subjects performed a locomotor adaptation task (perturbing force field applied to the ankle during swing using a robotized orthosis) on 2 consecutive days. Learning was assessed using kinematic measures (peak and mean absolute plantarflexion errors) and electromyographic (EMG) activity. Half of the participants performed the locomotor adaptation task with pain on Day 1 (capsaicin cream around the ankle), while the task was performed pain-free for all subjects on Day 2 to assess retention. Pain had no significant effect on baseline gait parameters nor on performance during the locomotor adaptation task (for either kinematic or EMG measures) on Day 1. Despite this apparently normal motor acquisition, pain-free Day 2 performance was markedly and significantly impaired in the Pain group, indicating that pain during training had an impact on the retention of motor memories (interfering with consolidation and/or retrieval). These results suggest that the same motor rehabilitation intervention could be less effective if administered in the presence of 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) | 0.000 | 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".