Case Report: Phantom limb pain relief after cognitive multisensory rehabilitation
Bibliographic record
Abstract
Introduction: Relieving phantom limb pain (PLP) after amputation in patients resistant to conventional therapy remains a challenge. While the causes for PLP are unclear, one model suggests that maladaptive plasticity related to cortical remapping following amputation leads to altered mental body representations (MBR) and contributes to PLP. Cognitive Multisensory Rehabilitation (CMR) has led to reduced pain in other neurologic conditions by restoring MBR. This is the first study using CMR to relieve PLP. Methods: A 26-year-old woman experienced excruciating PLP after amputation of the third proximal part of the leg, performed after several unsuccessful treatments (i.e., epidural stimulator, surgeries, analgesics) for debilitating neuropathic pain in the left foot for six years with foot deformities resulting from herniated discs. The PLP was resistant to pain medication and mirror therapy. PLP rendered donning a prosthesis impossible. The patient received 35 CMR sessions (2×/day during weekdays, October-December 2012). CMR provides multisensory discrimination exercises on the healthy side and multisensory motor imagery exercises of present and past actions in both limbs to restore MBR and reduce PLP. Results: After CMR, PLP reduced from 6.5-9.5/10 to 0/10 for neuropathic pain with only 4-5.5/10 for muscular pain after exercising on the Numeric Pain Rating Scale. McGill Pain Questionnaire scores reduced from 39/78 to 5/78, and Identity (ID)-Pain scores reduced from 5/5 to 0/5. Her pain medication was reduced by at least 50% after discharge. At 10-month follow-up (9/2013), she no longer took Methadone or Fentanyl. After discharge, receiving CMR as outpatient, she learned to walk with a prosthesis, and gradually did not need crutches anymore to walk independently indoors and outdoors (9/2013). At present (3/2024), she no longer takes pain medication and walks independently with the prosthesis without assistive devices. PLP is under control. She addresses flare-ups with CMR exercises on her own, using multisensory motor imagery, bringing the pain down within 10-15 min. Conclusion: The case study seems to support the hypothesis that CMR restores MBR which may lead to long-term (12-year) PLP reduction. MBR restoration may be linked to restoring accurate multisensory motor imagery of the remaining and amputated limb regarding present and past actions.
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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 | 0.001 |
| 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) | 0.004 | 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".