CERVICAL SPINAL CORD INJURY AND UPPER LIMB ROBOTIC THERAPY
Bibliographic record
Abstract
Abstract. A major debilitating factor of sustaining a cervical level spinal cord injury is the loss of independence in completing activities of daily living as a result of impaired upper limb function. Early intervention has been hypothesised to preserve upper limb function in this population and enhance capacity to perform functional tasks. The use of robotics as an upper limb therapy modality is increasing in the neurorehabilitation field, however there is limited evidence to support their use in the cervical spinal cord injury population. Despite this, occupational therapists are using them as part of a therapy program. Aim: This study aimed to explore the upper limb outcomes of using a computer assisted robotic device in acute therapy for people who have sustained a cervical spinal cord injury. Methods: A single case pre-post study design was performed with one middle aged male who had who was an inpatient at a public metropolitan hospital in Australia. They undertook a three week therapy program using the Diego by Tyromotion in conjunction with standard occupational therapy interventions. Range of motion, muscular strength, pain, fatigue the Spinal Cord Independence Measure, and the Canadian Occupational Performance Measure were used as outcome measures. Results: Increases were seen in range of motion and muscular strength and functional status; objective and subjectively. Conclusion: Preliminary findings suggest that the Diego may be a useful tool for improving upper limb outcomes when combined with occupational therapy in this population, however greater research and participants are required for definitive data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".