On the use of virtual reality for individuals with upper limb loss: a systematic scoping review
Bibliographic record
Abstract
INTRODUCTION: Virtual reality has recently become a popular application for rehabilitation and motor control research. This technology has emerged as a valid addition to conventional therapy and promises a successful rehabilitation. This study describes recent research related to the use of virtual reality applications in the rehabilitation of individuals with upper limb loss and to see whether this technology has enough proof of its applicability. EVIDENCE ACQUISITION: Searches were conducted with the Web of Science, Google Scholar, IEEE Xplore, and PubMed databases from inception up to September 2020. Articles that employed virtual reality in the rehabilitation of individual with upper limb loss were included in the research if it is written in English, the keyword exists in the title and abstract; it uses visual feedback in nonimmersive, semi-immersive, or fully immersive virtual environments. Data extraction was carried out by two independent researchers. The study was drafted using the Preferred Reporting Items for Systematic Reviews and Meta-analysis Protocols (PRISMA). EVIDENCE SYNTHESIS: A total of 38 articles met the inclusion criteria. Most studies were published between 2010 and 2020. Thirty-nine percent of the studies (N.=15), originates from North America; 55% of the studies (N.=21), were publicly funded; 61% of the studies (N.=24), was without disclosure of conflict of interest; 82% of the studies (N.=31), were cited in other studies. All the studies were published in journals and conference proceedings. Sixty-six percent of the studies (N.=25) has come out with positive outcome. The design studies were mostly case reports, case series, and poorly designed cohort studies that made up 55% (N.=21) of all the studies cited here. CONCLUSIONS: The research conducted on the use of virtual reality in individual with upper limb loss rehabilitation is of very low quality. The improvements to the research protocol are much needed. It is not necessary to develop new devices, but rather to assess existing devices with well-conducted randomized controlled trials.
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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 | 0.030 | 0.116 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.022 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".