Promoting Gait Recovery and Limiting Neuropathic Pain After Spinal Cord Injury
Bibliographic record
Abstract
Most persons living with a spinal cord injury experience neuropathic pain in the months following their lesion, at the moment where they receive intensive gait rehabilitation. Based on studies using animal models, it has been proposed that central sensitization in nociceptive pathways (maladaptive plasticity) and plasticity related to motor learning (adaptive plasticity) share common neural mechanisms and compete with each other. This article aims to address the discrepancy between the growing body of basic science literature supporting this hypothesis and the general belief in rehabilitation research that pain and gait rehabilitation represent two independent problems. First, the main findings from basic research showing interactions between nociception and learning in the spinal cord will be summarized, focusing both on evidence demonstrating the impact of nociception on motor learning and of motor learning on central sensitization. Then, the generalizability of these findings in animal models to humans will be discussed. Finally, the way potential interactions between nociception and motor learning are currently taken into account in clinical research in patients with spinal cord injury will be presented. To conclude, recommendations will be proposed to better integrate findings from basic research into future clinical research in persons with spinal cord injury.
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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 | 0.000 |
| 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".