Use of virtual reality for the management of phantom limb pain: a systematic review
Bibliographic record
Abstract
PURPOSE: To summarize the research on the effectiveness of virtual reality (VR) therapy for the management of phantom limb pain (PLP). METHODS: Three databases (SCOPUS, Ovid Embase, and Ovid MEDLINE) were searched for studies investigating the use of VR therapy for the treatment of PLP. Original research articles fulfilling the following criteria were included: (i) patients 18 years and older; (ii) all etiologies of amputation; (iii) any level of amputation; (iv) use of immersive VR as a treatment modality for PLP; (v) self-reported objective measures of PLP before and after at least one VR session; (vi) written in English. RESULTS: A total of 15 studies were included for analysis. Fourteen studies reported decreases in objective pain scores following a single VR session or a VR intervention consisting of multiple sessions. Moreover, combining VR with tactile stimulation had a larger beneficial effect on PLP compared with VR alone. CONCLUSIONS: Based on the current literature, VR therapy has the potential to be an effective treatment modality for the management of PLP. However, the low quality of studies, heterogeneity in subject population and intervention type, and lack of data on long-term relief make it difficult to draw definitive conclusions.IMPLICATION FOR REHABILITATIONVirtual reality (VR) therapy has emerged as a new potential treatment option for phantom limb pain (PLP) that circumvents some limitations of mirror therapy.VR therapy was shown to decrease PLP following a single VR session as well as after an intervention consisting of multiple sessions.The addition of vibrotactile stimuli to VR therapy may lead to larger decreases in PLP scores compared with VR therapy alone.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".