Somatosensory Feedback to the Cheek During Virtual Visual Feedback Therapy Enhances Pain Alleviation for Phantom Arms
Bibliographic record
Abstract
BACKGROUND: Patients who suffer from phantom limb pain can perceive tactile stimuli applied to the cheek on their affected side as if it were coming from their phantom limb, a phenomenon called "referred sensation." OBJECTIVES: To investigate the analgesic effect produced by tactile feedback provided to the cheek during neurorehabilitation using visual feedback. METHODS: Nine participants with phantom upper limb pain performed virtual reality neurorehabilitation exercises in which they repeatedly touched a target object with a virtual representation of their affected limb. We applied tactile feedback to their cheek when their virtual affected limb touched a virtual object (Cheek Condition). We also included 2 control conditions where tactile feedback was either applied to their intact hand (Intact Hand Condition) or not applied at all (No Stimulus Condition). We evaluated pain intensity on an 11-point rating scale and pain quality using the short-form McGill Pain Questionnaire before and after each rehabilitation condition. RESULTS: The median pain-reduction rate in the Cheek Condition (33.3 ± 24.4%) was significantly higher than in the Intact Hand Condition (16.7 ± 12.3%) and the No Stimulus Condition (12.5 ± 13.5%; P < .05). Even patients who did not feel referred sensations reported significant pain reduction after the Cheek Condition. CONCLUSIONS: The analgesic effect of neurorehabilitative visual feedback during phantom limb movement is significantly improved by applying somatosensory feedback to the cheek on the affected side. Further studies are needed to extend these findings to objective pain measures and to elucidate the neural mechanisms that underlie the analgesic effect.
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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) | 0.004 | 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".