Effect of load during electrical stimulation training in spinal cord injury
Bibliographic record
Abstract
Electrical stimulation training is known to alter skeletal muscle characteristics after a spinal cord injury, but the effect of load on optimizing the training protocol has not been fully investigated. This study investigated two electrical-stimulation training regimes with different loads on intramuscular parameters of the paralyzed lower limbs. Six paraplegic individuals with a spinal cord injury underwent electrical stimulation training (45 min daily for 3 days per week for 10 weeks). One leg was trained statically with load, and the contralateral leg was trained dynamically with minimal load. Isometric force assessed with 35-HZ stimuli increased significantly in both legs from baseline, with the static-trained leg also being significantly higher than the dynamic-trained leg. The vastus lateralis muscle of the statically trained leg showed a significant increase in type I fibers, fiber cross-sectional area, capillary-to-fiber ratio, and citrate synthase activity when compared to both baseline and the dynamically trained leg. Relative oxygenation of the vastus lateralis muscle as determined by near infrared spectroscopy was also significantly greater after static training. This study indicates that the load that is applied to paralyzed muscle during an electrical stimulation training program is an important factor in determining the amount of muscle adaptation that can be achieved.
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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 | 0.001 |
| 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".