Treating Lower Phantom Limb Pain in the Postoperative Acute Care Setting Using Virtual Reality: Protocol for a 4-Phase Development and Feasibility Trial
Bibliographic record
Abstract
BACKGROUND: Phantom limb pain (PLP) affects most people living with lower limb amputations (LLAs). Nonpharmacological interventions, such as graded motor imagery (GMI), have demonstrated promise as PLP treatments. However, GMI access is limited by low patient buy-in and long public outpatient wait times. Considering PLP has been shown to be more prevalent and severe immediately following LLA, there is an urgent need to bypass barriers to allow for prompt access to PLP interventions. In response to this need, the multidisciplinary research team in this study developed a virtual reality (VR) program that administers GMI treatment. This novel intervention may be completed independently and promptly within the postoperative acute care setting. Before conducting a randomized controlled trial, the VR-GMI program must be developed and refined through a rigorous and multistage feasibility assessment. OBJECTIVE: This protocol aims to outline the development and feasibility of the VR-GMI prototype for treating people with LLAs in the postoperative acute care setting (ie, inpatient and home settings) through an iterative, patient-centered, and descriptive approach. METHODS: Four phases of prototype development and assessment were conducted. In phase 1 (completed), the VR-GMI prototype was developed in collaboration with engineers at the National Research Council and in consultation with patient partners. In phase 2 (completed), people with lived experience with amputations were recruited from local physiotherapy and prosthetic clinics to trial the VR-GMI program and provide feedback through semistructured interviews and self-report measures. Phase 3 (completed) consisted of a descriptive case series of individuals who trialed the VR-GMI prototype immediately following their LLAs in the hospital. Results from phase 3 informed the development of a primary quantitative feasibility study. Phase 4 (underway) aims to evaluate the acceptability and pilot outcomes of the VR-GMI program in hospital and home settings as well as improve study procedures for a future randomized controlled trial (phase 4A). Iterative developments were made to the VR-GMI program between each phase to improve prototype fidelity. These iterative developments will also be reviewed in a series of focus groups to finalize the VR-GMI prototype (phase 4B). RESULTS: Recruitment for phases 1 and 2 was completed in September 2023. Phase 3 was completed in July 2024, and phase 4A is currently underway with 15 participants recruited as of March 2025. CONCLUSIONS: The intervention developed is the first VR PLP treatment implementing GMI and prioritizing an in-depth, patient-centered approach before assessing its efficacy. Doing so will improve the likelihood of successful clinical implementation. Moreover, very few PLP interventions have been assessed in the acute postoperative period when they may prevent PLP before its onset. TRIAL REGISTRATION: ClinicalTrials.gov NCT06638918; https://clinicaltrials.gov/study/NCT06638918. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/68008.
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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 | 0.039 | 0.038 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.053 | 0.011 |
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".