An Intensive Virtual Reality Program Improves Functional Balance and Mobility of Adolescents With Cerebral Palsy
Bibliographic record
Abstract
In Brief Purpose: To examine functional balance and mobility in adolescents with cerebral palsy classified at Gross Motor Function Classification System (GMFCS) level I following an intensive short-duration virtual reality (VR) intervention. Methods: Single-subject, multiple-baseline design with 4 adolescents. Outcomes included the Community Balance and Mobility Scale (CB&M), the 6-Minute Walk Test (6MWT), the Timed Up and Down Stairs, and the Gross Motor Function Measure Dimension E. Assessments were recorded 3 to 6 times at baseline, 5 times during intervention, and 4 times at follow-up. Daily 90-minute VR intervention was completed for 5 consecutive days. Visual, statistical, and clinical significance analyses were used. Results: Statistically significant improvements were shown in all adolescents on CB&M and 6MWT. True change was recorded in all for the CB&M and in 3 for the 6MWT. Conclusions: Functional balance and mobility in adolescents with cerebral palsy classified at GMFCS level I improve with intense, short duration VR intervention, and changes are maintained at 1-month posttraining. These investigators report that functional balance and mobility in adolescents with cerebral palsy classified at GMFCS level I improved with intense, short duration VR intervention, and the improvements continued to be present at 1-month post training.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".