Exoskeleton Training for Spinal Cord Injury Neuropathic Pain (ExSCIP): Protocol for a Phase 2 Feasibility Randomised Trial
Bibliographic record
Abstract
Background: Following Spinal Cord Injury (SCI), 53% of people develop neuropathic pain (NP). NP can be more debilitating than other consequences of SCI, and a persistent health issue. Pharmacotherapies are commonly recommended for NP management in SCI, although severe pain often remains refractory to these treatments in many sufferers. Furthermore, poor medication adherence exists, stemming from unacceptable side-effects and fear of dependency.Sensorimotor stimulation using active walking with robotic assistance has not been well studied in NP after SCI, despite convincing locomotor-based pre-clinical studies, identifying prevention and reversal of NP.Our primary aim is to assess the impact of exoskeleton-based walking on NP intensity and interference after SCI and examine feasibility outcomes for progression to a definitive trial. Methods: This is a phase 2 single-blinded, randomised feasibility study. It will test the feasibility and acceptability of exoskeleton-based walking 3 times per week for 12 weeks (intervention), as a mechanistic-based intervention for NP after SCI. The comparator will be an equally dosed, blended relaxation programme devoid of motor imagery prompts. 40 participants with moderate-to-severe NP post SCI will be recruited and randomised to intervention and comparator groups.The primary outcomes are feasibility outcomes for progression to definitive trial which include recruitment and retention rates, adverse events and acceptability of the intervention.Secondary outcomes explore changes in NP intensity and interference as measured by the International Spinal Cord Injury Pain Basic Data Set 3.0 (ISCIPBDS) at baseline, post-intervention (week 13) and at 6-month follow-up. Conclusions: There is a need to explore non-pharmacological management of NP after SCI. The findings of this feasibility trial will inform the development of a future multicentre, international RCT. Trial Registration: NCT06463418, 08/07/2024, https://clinicaltrials.gov/study/NCT06463418.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.105 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".