Virtual and Augmented Reality in Management of Phantom Limb Pain: A Systematic Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Upper and lower limb amputations are frequently associated with phantom limb pain (PLP). Recently, virtual reality (VR) and augmented reality (AR) have been reported as a potential therapy of PLP. We have conducted a systematic review of literature to evaluate the efficacy of VR and AR in managing PLP. Four databases were searched: PubMed, EMBASE, Cumulative Index to Nursing and Allied Health Literature, and Web of Science. We utilized the Preferred Reporting Items for Systematic Reviews and Meta-Analysis for our organization. The initial search resulted in 164 results. After title, abstract, and full-text screening, 9 studies were included. One study was of good quality and 8 studies were of fair to poor quality. Seven studies utilized VR and 2 studies utilized AR. The number of treatment sessions ranged from 1 to 28 and the duration ranged from 10 minutes to 2 hours. Several pain scales were used to evaluate PLP pre- and postintervention including Numeric Rating Scale, Pain Rating Index, McGill Pain Questionnaire, and Visual Analog Scale. All the studies reported improvement of PLP on one or more of pain scales after one or more sessions of VR or AR. Despite the promising results reported by literature, we cannot recommend using VR or AR for PLP. Most of the studies are of poor design and have limited sample size with high bias levels. Therefore, no substantial evidence can be derived from them. However, we do believe further research with high-quality randomized controlled trials should take place to increase the knowledge of the potential advantages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it