Locomotor adaptations and aftereffects to resistance during walking in individuals with spinal cord injury
Bibliographic record
Abstract
Muscle activity during the swing phase of walking is influenced by proprioceptive feedback pathways. Previous studies have shown that feedback and anticipatory motor commands contribute to locomotor adaptive strategies to prolonged exposure to a resistance against leg movements during walking. The purpose of this study was to determine whether people with motor-incomplete spinal cord injuries (SCI) modulate flexor muscle activity in response to different levels of resistance in a similar way as uninjured controls. A second purpose was to determine whether people with motor-incomplete SCI have the capacity to form anticipatory motor commands following exposure to resistance. Subjects walked on a treadmill with the Lokomat robotic gait orthosis. The Lokomat applied different levels of a velocity-dependent resistance, normalized to each subject's maximum voluntary contraction of the hip flexors. Each condition consisted of 20 steps against resistance followed by 20 steps without. Electromyography and kinematics of the lower limb were recorded. Although both groups responded to the resistance with an overall increase in rectus femoris activity during swing, the SCI group showed weak modulation of muscle activity to different levels of resistance. Following removal of the resistance, both groups showed aftereffects, but they were manifested differently. Controls responded to the removal of resistance with a high step, whereas the SCI subjects exhibited increased step length. The size of the aftereffect was related to the amount of added resistance. In addition, the SCI group showed a negative relationship between the size of the aftereffect and locomotor 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 | 0.000 | 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".