Student Competition (Knowledge Generation) ID 1985159
Bibliographic record
Abstract
Background When we walk, we swing our arms in rhythm with our legs due to neural connections in the spinal cord. Currently, rehabilitation protocols primarily focus on leg training to improve walking function. However, past research has highlighted the importance of engaging the arms in rehabilitative interventions through arm and leg cycling following an incomplete spinal cord injury (iSCI). Objectives The objective of this study is to determine if the application of non-invasive transcutaneous spinal cord stimulation (tSCS) paired with arm and leg cycling can improve walking for individuals with an iSCI. It is hypothesized that the addition of tSCS will activate previously inaccessible neural circuits within the spinal cord to produce significant improvements in walking capacity. Methods Individuals with an iSCI underwent arm and leg cycling training that was assisted by functional electrical stimulation applied to the main extensor muscles of the leg. tSCS was applied over the cervical and lumbar regions of the spinal cord. Training consisted of 1 hour of cycling, 5 days/week for 12 weeks. A series of clinical and biomechanical assessments were performed to assess changes in functional mobility. Results Preliminary results in three participants showed increases in maximal walking speed on the 10-metre walk test, increases in walking endurance on the 6-minute walk test, and improved balance on the Berg Balance Scale. Preliminary biomechanical results suggested that the addition of tSCS may reduce left-right asymmetry. Conclusions Arm and leg cycling paired with tSCS offers tremendous potential for improvements in walking function following an iSCI.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.890 | 0.813 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".