The efficacy of interactive, motion capture-based rehabilitation on functional outcomes in an inpatient stroke population: a randomized controlled trial
Bibliographic record
Abstract
OBJECTIVE: To compare the efficacy of novel interactive, motion capture-rehabilitation software to usual care stroke rehabilitation on physical function. DESIGN: Randomized controlled clinical trial. SETTING: Two subacute hospital rehabilitation units in Australia. PARTICIPANTS: In all, 73 people less than six months after stroke with reduced mobility and clinician determined capacity to improve. INTERVENTIONS: Both groups received functional retraining and individualized programs for up to an hour, on weekdays for 8-40 sessions (dose matched). For the intervention group, this individualized program used motivating virtual reality rehabilitation and novel gesture controlled interactive motion capture software. For usual care, the individualized program was delivered in a group class on one unit and by rehabilitation assistant 1:1 on the other. MAIN MEASURES: Primary outcome was standing balance (functional reach). Secondary outcomes were lateral reach, step test, sitting balance, arm function, and walking. RESULTS: Participants (mean 22 days post-stroke) attended mean 14 sessions. Both groups improved (mean (95% confidence interval)) on primary outcome functional reach (usual care 3.3 (0.6 to 5.9), intervention 4.1 (-3.0 to 5.0) cm) with no difference between groups ( P = 0.69) on this or any secondary measures. No differences between the rehabilitation units were seen except in lateral reach (less affected side) ( P = 0.04). No adverse events were recorded during therapy. CONCLUSION: Interactive, motion capture rehabilitation for inpatients post stroke produced functional improvements that were similar to those achieved by usual care stroke rehabilitation, safely delivered by either a physical therapist or a rehabilitation assistant.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.183 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".