A computer-game-based rehabilitation platform for individuals with fine and gross motor upper extremity deficits post-stroke (CARE fOR U) – Protocol for a randomized controlled trial
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND & PURPOSE: Activity-based neuroplasticity and re-organization leads to motor learning via replicating real-life movements. Increased repetition of such movements has growing evidence over last few decades. In particular, computer-game-based rehabilitation is found to be effective, feasible and acceptable for post-stroke upper limb deficits. Our study aims to evaluate the feasibility and effectiveness of 12 weeks of computer-game-based rehabilitation platform (GRP) on fine and gross motor skills post-stroke in India. METHODS: Through this trial we will study the effect of adjunctive in-hospital GRP (using a motion-sensing airmouse with off-the-shelf computer games) in 80 persons with subacute stroke, for reduction of post-stroke upper limb deficits in a single-centre prospective Randomized Open, Blinded End- point trial when compared to conventional therapy alone. RESULTS: We intend to evaluate between-group differences using Wolf Motor Function test, Stroke Specific Quality of Life, and GRP assessment tool. Feasibility will be assessed via recruitment rates, adherence to intervention periods, drop-out rate and qualitative findings of patient experience with the intervention. CONCLUSION: The CARE FOR U trial is designed to test the feasibility and effectiveness of a computer-game based rehabilitation platform in treating upper limb deficits after stroke. In case of positive findings GRP can be widely applicable for stroke populations needing intensive and regular therapy with supervision.
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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 | 0.003 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it