Game-based Exercises for Dynamic Short-Sitting Balance Rehabilitation of People With Chronic Spinal Cord and Traumatic Brain Injuries
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Goal-oriented, task-specific training has been shown to improve function; however, it can be difficult to maintain patient interest. This report describes a rehabilitation protocol for the maintenance of balance in a short-sitting position following spinal cord and head injuries by use of a center-of-pressure-controlled video game-based tool. The scientific justification for the selected treatment is discussed. CASE DESCRIPTION: Three adults were treated: 1 young adult with spina bifida (T10 and L1-L2), 1 middle-aged adult with complete paraplegia (complete lesion at T11-L1), and 1 middle-aged adult with traumatic brain injury. All patients used wheelchairs full-time. OUTCOMES: The patients showed increased motivation to perform the game-based exercises and increased dynamic short-sitting balance. DISCUSSION: The patients exhibited increases in practice volume and attention span during training with the game-based tool. In addition, they demonstrated substantial improvements in dynamic balance control. These observations indicate that a video game-based exercise approach can have a substantial positive effect by improving dynamic short-sitting balance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".