Faculty Opinions recommendation of Virtual and augmented reality in the treatment of phantom limb pain: A literature review.
Bibliographic record
Abstract
BACKGROUND: Phantom limb pain (PLP), the perception of discomfort in a limb no longer present, commonly occurs following amputation. A variety of interventions have been employed for PLP, including mirror therapy. Virtual Reality (VR) and augmented reality (AR) mirror therapy treatments have also been utilized and have the potential to provide an even greater immersive experience for the amputee. However, there is not currently a consensus on the efficacy of VR and AR therapy.OBJECTIVE: The aim of this review is to evaluate and summarize the current research on the effect of immersive VR and AR in the treatment of PLP.METHODS: A comprehensive literature search was conducted utilizing PubMed and Google Scholar in order to collect all available studies concerning the use of VR and/or AR in the treatment of PLP using the search terms "virtual reality," "augmented reality," and "phantom limb pain." Eight studies in total were evaluated, with six of those reporting quantitative data and the other two reporting qualitative findings.RESULTS: All studies located were of low-level evidence. Each noted improved pain with VR and AR treatment for phantom limb pain, through quantitative or qualitative reporting. Additionally, adverse effects were limited only to simulator sickness occurring in one trial for one patient.CONCLUSIONS: Despite the positive findings, all of the studies were confined purely to case studies and case report series. No studies of higher evidence have been conducted, thus considerably limiting the strength of the findings. As such, the current use of VR and AR for PLP management, while attractive due to the increasing levels of immersion, customizable environments, and decreasing cost, is yet to be fully proven and continues to need further research with higher quality studies to fully explore its benefits. PMID: 28211829
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.068 | 0.015 |
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".