The GestureTek virtual reality system in rehabilitation: a scoping review
Bibliographic record
Abstract
PURPOSE: To identify, to map out and to appraise research examining GestureTek virtual reality (VR) use for physical and cognitive rehabilitation and to highlight areas for future research. METHOD: Scoping review methodology was used to systematically and comprehensively search the academic and grey literature for GestureTek-specific research. Consensus was achieved following two reviewers' independent inclusion screening, data extraction and appraisal. RESULTS: Forty-four studies evaluated the utility and efficacy of the IREX, GX, VMall and Meal Maker software for rehabilitation across a range of populations, with few adverse events reported. Stroke and cerebral palsy research dominated, while geriatrics was largely overlooked. Efficacy research provided support for balance, mobility, upper extremity, cognitive, fitness and daily living skills outcomes for specific populations with effect sizes ranging from 0.19 to 3.34. Nevertheless, few strong quality or high-level studies are available, and no clear guidelines on optimal treatment characteristics exist. Outcomes of primary interest were at ICF body function and activity levels; evaluation of transfer of training across ICF levels is needed. CONCLUSIONS: This literature provides preliminary evidence for the technology's efficacy for rehabilitation. Identified research gaps relate to study quality and design, treatment characteristics, populations and outcomes of interest, including transfer across ICF levels. IMPLICATIONS FOR REHABILITATION: There is limited high-level evidence to support the use of the GestureTek virtual reality system for rehabilitation. Moderate to strong quality evidence exists primarily for the stroke and cerebral palsy populations, and primarily for balance, mobility and upper extremity outcomes. A lack of consistent outcome measures in this body of literature has created "silos" of research that cannot be quantitatively combined using a meta-analytic strategy.
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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 | 0.025 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.025 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".