Could enhanced reflex function contribute to improving locomotion after spinal cord repair?
Bibliographic record
Abstract
Although numerous treatments have been found to improve locomotion in spinal cord injured mammals, the underlying mechanisms are very poorly understood. Some of the main possibilities are: (1) regeneration of axons across the injury site and the re-establishment of descending pathways needed to voluntarily initiate and maintain stepping in the hind legs, (2) enhanced effectiveness of undamaged neurons in preparations with incomplete transections of the cord, (3) non-specific facilitation of reflexes and intrinsic spinal networks by transmitters released from regenerated axons and/or by substances introduced by the treatment, and (4) enhanced trunk movements close to the injury site strengthening the mechanical coupling of the trunk to the hind legs via spinal reflexes. In addition, any procedure that even slightly improves stepping may be further enhanced by use-dependent modification of reflex pathways and interneuronal networks in the lumbar cord. The emphasis of this review is on the contribution of spinal reflexes to the patterning of motor activity for walking, and how enhancing reflex function may contribute to the improvement of locomotion by treatments aimed at restoring locomotion after complete transection of the spinal cord.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".