Extended reality used in the treatment of phantom limb pain: a multicenter, double-blind, randomized controlled trial
Bibliographic record
Abstract
ABSTRACT: Phantom limb pain (PLP) represents a significant challenge after amputation. This study investigated the use of phantom motor execution (PME) and phantom motor imagery (PMI) facilitated by extended reality (XR) for the treatment of PLP. Both treatments used XR, but PME involved overt execution of phantom movements, relying on the decoding of motor intent using machine learning to enable real-time control in XR. In contrast, PMI involved mental rehearsal of phantom movements guided by XR. The study hypothesized that PME would be superior to PMI. A multicenter, double-blind, randomized controlled trial was conducted in 9 outpatient clinics across 7 countries. Eighty-one participants with PLP were randomly assigned to PME or PMI training. The primary outcome was the change in PLP, measured by the Pain Rating Index, from baseline to treatment cessation. Secondary outcomes included various aspects related to PLP, such as the rate of clinically meaningful reduction in pain (CMRP; >50% pain decrease). No evidence was found for superiority of overt execution (PME) over imagery (PMI) using XR. PLP decreased by 64.5% and 68.2% in PME and PMI groups, respectively. Thirty-seven PME participants (71%) and 19 PMI participants (68%) experienced CMRP. Positive changes were recorded in all other outcomes, without group differences. Pain reduction for PME was larger than previously reported. Despite our initial hypothesis not being confirmed, PME and PMI, aided by XR, are likely to offer meaningful PLP relief to most patients. These findings merit consideration of these therapies as viable treatment options and alternatives to pharmacological treatments.
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.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".