Is virtual reality beneficial for dual-task gait training in patients with Parkinson's disease? A systematic review
Bibliographic record
Abstract
This systematic review examined the evidence about the effects of virtual reality (VR) on dual-task gait training in Parkinson's disease (PD). METHODS: this study (PROSPERO registration CRD42019114736) aimed to answer the question: "Is VR beneficial for dual-task gait training in patients with PD?" We searched for studies from 2008 to 2018 on Medline/PubMed and Web of Science/Web of knowledge databases. The keywords were Parkinson AND gait training AND virtual reality OR Parkinson AND gait training AND game. A total of 55 articles were retrieved, of which 11 systematic reviews, 11 opinions, letters to the editor, posters or conferences abstracts and 17 studies not evaluating the effects of VR gait training were excluded. Three further studies addressing VR dual-task gait training in PD (found in references of studies selected) were also included. Therefore, 19 studies were included and analysed. RESULTS: all studies reported gait improvement after VR training. Many clinical scales were used, hampering comparison of the effects of each protocol. CONCLUSION: VR dual-task gait training should be part of rehabilitation protocols for PD. The studies showed that VR training was effective, although specific guidelines have not yet been established.
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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) | 0.007 | 0.006 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".