Supraspinal Control Predicts Locomotor Function and Forecasts Responsiveness to Training after Spinal Cord Injury
Bibliographic record
Abstract
Restoration of walking ability is an area of great interest in the rehabilitation of persons with spinal cord injury. Because many cortical, subcortical, and spinal neural centers contribute to locomotor function, it is important that intervention strategies be designed to target neural elements at all levels of the neuraxis that are important for walking ability. While to date most strategies have focused on activation of spinal circuits, more recent studies are investigating the value of engaging supraspinal circuits. Despite the apparent potential of pharmacological, biological, and genetic approaches, as yet none has proved more effective than physical therapeutic rehabilitation strategies. By making optimal use of the potential of the nervous system to respond to training, strategies can be developed that meet the unique needs of each person. To complement the development of optimal training interventions, it is valuable to have the ability to predict future walking function based on early clinical presentation, and to forecast responsiveness to training. A number of clinical prediction rules and association models based on common clinical measures have been developed with the intent, respectively, to predict future walking function based on early clinical presentation, and to delineate characteristics associated with responsiveness to training. Further, a number of variables that are correlated with walking function have been identified. Not surprisingly, most of these prediction rules, association models, and correlated variables incorporate measures of volitional lower extremity strength, illustrating the important influence of supraspinal centers in the production of walking behavior in humans.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".