Effect of a robotic rehabilitation device on upper limb function in a sub-acute cervical spinal cord injury population
Bibliographic record
Abstract
Robotic rehabilitation devices have been suggested as a tool to increase the amount of rehabilitation delivered after a neurological injury. Clinical robotic rehabilitation studies of the upper extremity have generally focused on stroke survivors. We present the results of a multi-center pilot study where an upper-limb robotic rehabilitation device (Armeo Spring®, Hocoma AG) was incorporated into the rehabilitation program of 12 subjects with sub-acute cervical spinal cord injury (motor level C4-C6, AIS A-D). Outcomes were measured using two tests of upper extremity function: ARAT and GRASSP. The change in scores for the arm receiving the Armeo training were not statistically significant when compared to the arm not receiving the Armeo training at discharge from therapy and over follow up assessments (8.7 +/- 2.9 compared to 7.4 +/- 2.5 for ARAT at discharge, p = 0.98, and 13.0 +/- 3.2 compared to 13.3 +/- 3.3 for GRASSP at discharge, p = 0.69). Nevertheless, subjects with some minimal (partial) hand function at baseline had a significantly larger increase in GRASSP scores than subjects with no minimal hand function preserved at baseline (19.3 +/- 2.4 compared to 6.6 +/- 4.7, p = 0.02). This suggests that the initial functional capabilities of patients can influence the benefits measured after robotic rehabilitation training and heterogeneous subject populations should be avoided in early phase studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".