Adaptive changes of locomotion after central and peripheral lesions
Bibliographic record
Abstract
This paper reviews findings on the adaptive changes of locomotion in cats after spinal cord or peripheral nerve lesions. From the results obtained after lesions of the ventral/ventrolateral pathways or the dorsal/dorsolateral pathways, we conclude that with extensive but partial spinal lesions, cats can regain voluntary quadrupedal locomotion on a treadmill. Although tract-specific deficits remain after such lesions, intact descending tracts can compensate for the lesioned tracts and access the spinal network to generate voluntary locomotion. Such neuroplasticity of locomotor control mechanisms is also demonstrated after peripheral nerve lesions in cats with intact or lesioned spinal cords. Some models have shown that recovery from such peripheral nerve lesions probably involves changes at the supra spinal and spinal levels. In the case of somesthesic denervation of the hindpaws, we demonstrated that cats with a complete spinal section need some cutaneous inputs to walk with a plantigrade locomotion, and that even in this spinal state, cats can adapt their locomotion to partial cutaneous denervation. Altogether, these results suggest that there is significant plasticity in spinal and supraspinal locomotor controls to justify the beneficial effects of early proactive and sustained locomotor training after central (Rossignol and Barbeau 1995; Barbeau et al. 1998) or peripheral lesions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".