Robotic locomotor training in a low-resource setting: a randomized pilot and feasibility trial
Bibliographic record
Abstract
Activity-based Training (ABT) represents the current standard of neurological rehabilitation. Robotic Locomotor Training (RLT), an innovative technique, aims to enhance rehabilitation outcomes. This study aimed to conduct a randomized pilot and feasibility trial of a locomotor training program within South Africa. Individuals with chronic traumatic motor incomplete tetraplegia (n = 16). Each intervention involved 60-minute sessions, 3x per week, for 24-weeks. Outcomes included feasibility measures and functional capacity. 17 out of 110 individuals initiated the program (recruitment rate = 15.4%) and 16 completed the program (drop-out rate = 5.8%) and attended sessions (attendance rate = 93.9%). Both groups showed a significant increase in upper extremity motor score (MS) and abdominal strength post intervention. Only the RLT group showed a significant change in lower extremity MS, with a mean increase of 3.00 [0.00; 16.5] points over time. Distance walked in the Functional Ambulatory Inventory (SCI-FAI) increased significantly (p = 0.02) over time only for the RLT group. Feasibility rates of the intervention and functional outcomes justify a subsequent powered RCT comparing RLT to ABT as an effective rehabilitation tool for potentially improving functional strength and walking capacity in people with incomplete SCI. Spinal cord injury causes severe limitations to functional capacity, independence, and quality of life. Robotic Locomotor Training is growing rehabilitation modality for people with spinal cord injury, but currently its effects on functional capacity are limited. In a relatively small sample, this study shows that large, randomized control trials are feasible within a low-income setting. The preliminary findings of this study show that 12 weeks of locomotor training can improve ambulatory function and functional strength in individuals with spinal cord injury.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".