Clinical Feasibility and Preliminary Outcomes of a Novel Mixed Reality Based System to Manage Phantom Pain for Patients with Lower Limb Amputation.: A Pilot Study
Bibliographic record
Abstract
Abstract Objective This report describes the details of Mr. MAPP, a virtual mirror therapy system and the results of a clinical feasibility and evaluation study of a pilot sample of patients with history of lower limb amputation and phantom limb pain. Materials and Methods Mr. MAPP system uses a Kinect V2 RGB-D camera to capture the patient, generates a live interactive 3D model, and incorporates it into a virtual exergame. Mr. MAPP mirrors their preserved limb and allows participants to visualize their amputated limb virtually and perform their prescribed exercise program. Mr. MAPP creates a digital diary of their therapy sessions by recording game performance and errors. Ten consecutive patients with history of limb loss and phantom pain were targeted for this pilot study. They received a Mr. MAPP system and a laptop for home use for 1-month. Outcome measures including pain intensity and pain interference measured on Numerical Rating Scale (NRS) and McGill Pain Questionnaire (MPQ) as well as functional outcomes measured on Patient Specific Functional Scale (PSFS) were obtained at baseline, and after 1-month. Results Seven participants were enrolled, out of which four completed the study. The study was temporarily suspended due to restrictions imposed during the COVID-19 public health emergency. In-game performance data showed a trend towards improvement in most participants. Compared to baseline, NRS and MPQ scores at 1-month showed no clear trends. PSFS scores improved for 1 participant and remained unchanged for others. Conclusion Gamified home exercises performed via a novel, mixed-reality system—Mr. MAPP— using which virtual mirror therapy was delivered, is feasible and has potential to improve pain and pain related function in patients with lower limb amputations and phantom limb pain.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".