The Efficacy of Virtual Reality Training in the Rehabilitation of Orthopedic Ankle Injuries: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Introduction: Orthopedic ankle injuries are considered among the most common musculoskeletal injuries. A wide variety of modalities and techniques have been used for the management of these injuries, and virtual reality (VR) is one modality that has been examined in ankle injuries rehabilitation. Purpose: This study aims to systemically review previous studies evaluating the effect of virtual reality in rehabilitating orthopedic ankle injuries. Methods: We searched six online databases: PubMed, Web of Science (WOS), Scopus, the Physiotherapy Evidence Database (PEDro), Virtual Health Library (VHL), and Cochrane Central Register of Controlled Trials (CENTRAL). Results: = 0.30]. Compared with conventional physiotherapy, VR programs significantly improved gait parameters such as speed and cadence, muscle power, and perceived ankle instability; however, no significant difference was detected in the foot and ankle ability measure (FAAM). Additionally, significant improvements in static balance and perceived ankle instability were reported after the use of VR balance and strengthening programs. Finally, only two articles were deemed to have good quality, and the other studies' quality ranged from poor to fair. Conclusion: VR rehabilitation programs can be used to rehabilitate ankle injuries, as they are regarded as safe interventions and have promising effects. However, there is a need for studies with high quality since most included studies' quality varied from poor to fair.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.020 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".