Virtual reality in the rehabilitation of the arm after hemiplegic stroke: a randomized controlled pilot study
Bibliographic record
Abstract
OBJECTIVE: To assess the feasibility of a trial to investigate the effectiveness of virtual reality-mediated therapy compared to conventional physiotherapy in the motor rehabilitation of the arm following stroke, and to provide data for a power analysis to determine numbers for a future main trial. DESIGN: Pilot randomized controlled trial. SETTING: Clinical research facility. PARTICIPANTS: Eighteen people with a first stroke, 10 males and 8 females, 7 right and 2 left side most affected. Mean time since stroke 10.8 months. INTERVENTIONS: Participants were randomized to a virtual reality group or a conventional arm therapy group for nine sessions over three weeks. MAIN MEASURES: The upper limb Motricity Index and the Action Research Arm Test were completed at baseline, post intervention and six weeks follow-up. RESULTS: Outcome data were obtained from 95% of participants at the end of treatment and at follow-up: one participant withdrew. Compliance was high; only two people reported side-effects from virtual reality exposure. Both groups demonstrated small (7-8 points on upper limb Motricity Index and 4 points on the Action Research Arm Test), but non-significant, changes to their arm impairment and activity levels. CONCLUSION: A randomized controlled trial of virtual reality-mediated therapy comparable to conventional therapy would be feasible, with some suggested improvements in recruitment and outcome measures. Seventy-eight participants (39 per group) would be required for a main trial.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".