Locomotor and resistance training restore walking in an elderly person with a chronic incomplete spinal cord injury
Bibliographic record
Abstract
OBJECTIVE: To determine the effects of 10~weeks of locomotor training (LT) using body weight supported (BWS) treadmill training and resistance training (RT) programs on over-ground walking recovery, walking speed and distance, functional independent measure (FIM), walking index for spinal cord injury (WISCI) and Berg Balance Score in an elderly person with an incomplete spinal cord injury (SCI). DESIGN: A 66 year-old-male with a chronic incomplete SCI at C5/C6 ASIA Impairment Scale (AIS) D was admitted for rehabilitation following posterior laminectomy at L3-L5. The participant was a short distance ambulator relying primarily on his power wheelchair for mobility. He completed 10~weeks of LT using manual BWS treadmill twice weekly and RT for knee extensor muscle groups twice a week. A weekly test of the over-ground distance and speed were recoded over the course of the 10~weeks. Additionally, the participant underwent a three month evaluation after discharge. RESULTS: The 10-week program resulted in independent use of bilateral Canadian crutches to ambulate for 200 feet and increased over-ground walking speed. The FIM score increased from 3 to 6 and Berg balance score increased from 11 to 41. The WISCI score increased from 1 to 10. Three months post-discharge, the participant maintained his functional independency in sit to stand activity and over-ground walking. CONCLUSION: A combined program of LT and RT could enhance walking recovery in a person with a long-term SCI. The findings suggest that twice a week of LT can promote motor recovery if it is accompanied with an approach that effectively loads the paralyzed lower extremities.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".