Case Report: Virtual reality training for phantom limb pain after amputation
Bibliographic record
Abstract
Several reports have demonstrated the effectiveness of neurorehabilitation, such as mirror therapy or virtual reality, in treating phantom limb pain (PLP). This case study describes the effect of virtual reality training (VRT) on severe, long-term PLP and upper limb activity on the amputated side in a patient who underwent digit amputation 9 years prior. A woman in her 40 s underwent amputation of 2-5 fingers 9 years prior due to a workplace accident. She experienced persistent pain in the palms of her hand near the amputation sites. A single case design (ABA'B') was applied. Periods A and A' were set as periods without VRT intervention, and Periods B and B' were set as periods with VRT intervention. Periods A, B, A', and B' lasted 4, 10, 8, and 10 weeks, respectively. VRT was a task during which visual stimulation and upper limb movements were linked. The task consisted of catching a rolling ball in the display with a virtual hand, operated with both hands using a controller. VRT was performed once every 2-4 weeks for 30 min. Pain intensity was assessed using the short-form McGill Pain Questionnaire-2. Bilateral upper limb activity was measured continuously for 24 h using a triaxial accelerometer attached to the right and left wrist joints. The pain intensity was 147/220 points during Period A, 128 points during Period B, 93 points during Period A', and 100 points during Period B', showing a gradual decrease. Upper limb activity occurred mainly on the intact side during Periods A and B, whereas the activity on the amputated side increased 2-fold after Period A', and both upper extremities were used equally. Virtual reality training resulted in reduced pain intensity and increased activity in the upper limb. VRT may have induced reintegration of the sensory-motor loop, leading to a decrease in the PLP intensity. The upper limb activity on the amputated side may have also increased with the pain reduction. These results suggest that VRT may be valuable in reducing severe, long-term PLP.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".