Exoskeleton gait training to improve lower urinary tract function in people with motor-complete spinal cord injury: A randomized pilot trial
Bibliographic record
Abstract
OBJECTIVE: The primary aim of this study was to determine the feasibility of delivering an exoskeleton-assisted walking intervention targeting lower urinary tract function in people with motor-complete spinal cord injury. Secondary aims were to determine if exoskeleton walking activates the pelvic floor muscles, and compare 2 exoskeleton programmes regarding lower urinary tract function. DESIGN: Randomized pilot trial. SUBJECTS: Adults with motor-complete spinal cord injury at or above T10. METHODS: Participants were randomized to receive Ekso or Lokomat training. Feasibility outcomes included recruitment rate, adherence, and adverse events. Pelvic floor muscle electromyography was recorded during walking. Urodynamic studies, 3-day bladder diary, and Qualiveen-30 were administered pre- and post-training. RESULTS: Twelve people were screened and 6 people enrolled in the study. Two subjects withdrew from unrelated reasons. There was one adverse event. Pelvic floor muscle activity was greater in the Ekso group. Lower urinary tract function did not clearly change in either group. CONCLUSION: This pilot study demonstrates the feasibility of delivering an exoskeleton training programme targeting lower urinary tract function. Ekso-walking elicits pelvic floor muscle activity, but it remains unclear how locomotor training impacts lower urinary tract function.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".