Training With Virtual Visual Feedback to Alleviate Phantom Limb Pain
Bibliographic record
Abstract
BACKGROUND: Performing phantom movements with visual virtual feedback, or mirror therapy, is a promising treatment avenue to alleviate phantom limb pain. However the effectiveness of this approach appears to vary from one patient to another. OBJECTIVE: To assess the individual response to training with visual virtual feedback and to explore factors influencing the response to that approach. METHODS: Eight male participants with phantom limb pain (PLP) resulting from either a traumatic upper limb amputation or a brachial plexus avulsion participated in this single case multiple baseline study. Training was performed 2 times per week for 8 weeks where a virtual image of a missing limb performing different movements was presented and the participant was asked to follow the movements with his phantom limb. RESULTS: Patients reported an average 38% decrease in background pain on a visual analog scale (VAS), with 5 patients out of 8 reporting a reduction greater than 30%. This decrease in pain was maintained at 4 weeks postintervention in 4 of the 5 participants. No significant relationship was found between the long-term pain relief and the duration of the deafferentation or with the immediate pain relief during exposure to the feedback. CONCLUSIONS: These results support the use of training with virtual feedback to alleviate phantom limb pain. Our observations suggest that between-participant differences in the effectiveness of the treatment might be related more to a difference in the susceptibility to the virtual visual feedback, than to factors related to the lesion, such as the duration of the deafferentation.
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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) | 0.003 | 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".