Treating Lower Phantom Limb Pain in the Postoperative Acute Care Setting Through a Virtual Reality-Based Graded Motor Imagery Program: A Case Series
Bibliographic record
Abstract
Background: Phantom limb pain (PLP) affects over 70% of people with lower limb amputations (LLAs). Graded motor imagery (GMI) is an established nonpharmacological treatment that targets the cortical mechanisms associated with PLP onset and may be the most effective if it is administered shortly following amputation. However, application of GMI in practice is hindered by the need for trained health care professionals to assist with longitudinal administration and by low patient buy-in (i.e., one's understanding of, and motivation to engage in, a treatment modality). To address these barriers, our research team developed a virtual reality (VR) program to facilitate self-administration of GMI. Objectives: The primary objective of the current case series was to assess initial feasibility of using the VR GMI program in the acute postoperative setting by evaluating recruitment trends. The secondary objective was to describe the satisfaction, usability, and tolerability of the VR GMI program after short-term administration. Methods: Patients undergoing LLAs at a single academic tertiary hospital were screened for eligibility and interest, and consenting individuals underwent daily sessions in the VR GMI program. Adverse events (i.e., any instance in which pain or nausea interrupted VR engagement, as indicated by the Numerical Rating Scale) were monitored to evaluate tolerability. A self-report questionnaire assessed satisfaction and usability. Results: Of 52 individuals screened, 41 were not eligible to participate (79%). Five eligible individuals were not interested in participating due to feeling overwhelmed (10%). Six participants were recruited (12%), and four were able to complete the entire intervention before their hospital discharge (8%). Most participants rated the program as easy to understand, enjoyable, and helpful. However, participant scores also indicated the need to improve usability. Only one participant reported an adverse event (i.e., fatigue in phantom limb). Conclusions: These results will directly inform refinement of the VR GMI prototype and related VR research implemented in the postoperative setting.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".