Training With Virtual Visual Feedback to Alleviate Phantom Limb Pain
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.000 | 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 it