Home Use of an Upper Extremity Exoskeleton in Children with SMA: A Pilot Study
Bibliographic record
Abstract
Background: People with spinal muscular atrophy (SMA) often have arm weakness resulting in restricted independence and challenges with activities of daily living. An upper extremity (UE) orthosis, the Wilmington Robotic Exoskeleton (WREX), which augments arm movement by providing gravity assistance, was provided to a small cohort of subjects for 1 year. Resulting changes in the subjects’ performance were assessed. Method: Five subjects with SMA were asked to use the WREX system for 1 year. Data were collected at baseline and at 6-month intervals. Evaluation tools used were UE range of motion (ROM), the Box and Block Test, the Canadian Occupational Performance Measure (COPM), and the reachable surface area (RSA) using a Microsoft Kinect Sensor. Results: There were no significant changes in UE ROM without the device over time and no significant changes in dexterity after long-term use of the WREX. There were clinically meaningful changes in active ROM while wearing the device compared to without it and clinically meaningful changes in performance and satisfaction while wearing the device. The RSA software did not yield usable results for this population. Conclusion: Wearing bilateral WREX devices resulted in immediate improvements in ROM and function; however, the subjects experienced several barriers, which prevented consistent long-term use.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".