Neuromotor and Musculoskeletal Responses to Locomotor Training for an Individual With Chronic Motor Complete AIS-B Spinal Cord Injury
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: To determine the effects of locomotor training (LT) using body weight support (BWS), treadmill, and manual assistance on muscle activation, bone mineral density (BMD), and body composition changes for an individual with motor complete spinal cord injury (AIS B), 1 year after injury. METHODS: A man with chronic C6 AIS B (motor complete and sensory incomplete) spinal cord injury (SCI), 1 year after injury, completed 2 blocks of LT over a 9-month training period (35-session block followed by 8.6 weeks of no training and then a 62-session block). RESULTS: Before training, muscle activation was minimal for any muscle examined, whereas after the 2 blocks of LT (97 sessions), hip and knee muscle activation patterns for the bilateral rectus femoris, biceps femoris, and gastrocnemius were in phase with the kinematics. Mean EMG amplitude increased for all bilateral muscles and burst duration increased for rectus femoris and gastrocnemius muscles, whereas burst duration decreased for the biceps femoris after 62 LT sessions. Before LT, left biceps femoris had a pattern that reflected muscle stretch, whereas after training, muscle stretch of the left biceps femoris could not totally account for mean EMG amplitude or burst duration. After the 62 training sessions, total BMD decreased (1.54%), and regional BMD decreased (legs: 6.72%). Total weight increased, lean mass decreased (6.6%), and fat mass increased (7.4%) in the arms, whereas fat mass decreased (3.5%) and lean mass increased (4%) in the legs. CONCLUSIONS: LT can induce positive neural and body composition changes in a nonambulatory person with chronic SCI, indicating that neuromuscular plasticity can be induced by repetitive locomotor training after a motor complete SCI.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".