Using visual illusion to reduce at-level neuropathic pain in paraplegia
Bibliographic record
Abstract
Neuropathic pain after spinal cord injury is not well understood and is difficult to treat. One possible cause is mismatch between motor commands and sensory feedback. This two-part study in five paraplegic patients investigated whether a visual illusion aimed to correct this mismatch reduces pain. In study 1, patients undertook three conditions: (i) virtual walking: with a mirror placed in front of a screen, patients aligned their own upper body with a film of a lower body walking. Patients imagined walking and 'watched themselves' walk; (ii) guided imagery; (iii) watching a film. One patient withdrew from virtual walking because of distress. For all patients, the mean (95% CI) decrease in pain (100 mm VAS) was 42 mm (approximately 65%) (11-73 mm) for virtual walking, 18 mm (4-31 mm) for guided imagery and 4mm (-3 to 11 mm) for watching the film. Mean (95% CI) time to return to pre-task pain was 34.9 min (20.1-49.8 min) for virtual walking; 13.9 min (-0.9 to 28.8 min) for the guided imagery and 16.3 min (1.5-31.2 min) for the film. To investigate its clinical utility, four patients underwent virtual walking every weekday for 3 weeks. Mean (95% CI) decrease in pain was 53 mm (45-61 mm) at post training and 43 mm (27-58 mm) at 3-month follow-up. Virtual walking may be a viable treatment for pain after spinal cord injury. A clinical trial seems warranted.
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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".