Visuomotor Integration Assessment Using Immersive Virtual Reality for Children With Cerebral Palsy: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: Visuomotor integration (VMI) impairments are common in children with cerebral palsy (CP) and can impact performance of goal-directed upper-extremity tasks. VMI impairment is clinically assessed using the gold-standard Beery-Buktenica test, whereas research paradigms use computerized assessments incorporating eye and hand movement tracking with touchscreen displays. Immersive virtual reality (VR) may potentially enable more ecologically valid VMI assessments through the inclusion of 3D tasks and visual distractions. However, the potential of immersive VR as a VMI assessment method in children with CP has not been evaluated. The current study aims to investigate how VR can assess VMI impairments in children with CP. METHODS: Twelve children with CP completed the Beery-Buktenica VMI test and performed eye-only, hand-only and eye-hand VMI tasks in touchscreen, visually simple VR and visually complex VR conditions. Eye and hand endpoint accuracy and task completion time quantified VMI performance. We compared performance on each task and in each environment between children with below- versus above-average Beery-VMI scores. RESULTS: There were no significant relationships between Beery-VMI score and eye-hand task performance in visually simple VR. Compared to the touchscreen task, participants demonstrated significantly reduced eye and hand endpoint accuracy in visually simple VR, with no difference between Beery-VMI groups. Children with below-average Beery-VMI scores decreased eye endpoint accuracy and increased trial completion time in visually complex VR. CONCLUSION: Findings from this pilot study do not support immersive VR as a VMI assessment method in children with CP.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".