The sensorimotor effects of a lower limb proprioception training intervention in individuals with a spinal cord injury
Bibliographic record
Abstract
Proprioception is critical for movement control. After a spinal cord injury (SCI), individuals not only experience paralysis but may also experience proprioceptive deficits, further confounding motor recovery. The objective of this study was to test the effects of a robotic-based proprioception training protocol on lower limb proprioceptive sense in people with incomplete SCI. A secondary objective was to assess whether the effects of training transferred to a precision stepping task in people with motor-incomplete SCI. Participants with chronic incomplete SCI and able-bodied controls underwent a 2-day proprioceptive training protocol using the Lokomat robotic exoskeleton. The training involved positioning the test leg to various positions and participants were asked to report whether they felt their heel position (end-point position) was higher or lower compared with a reference position. Feedback was provided after each trial to help participants learn strategies that could help them discern different positions of their foot. Changes in end-point position as well as knee joint position sense were assessed pre- and posttraining. We also assessed the effects of proprioception training on the performance of a precision stepping task in people with motor-incomplete SCI. Following training, there were significant improvements in end-point and knee joint position sense in both groups. The magnitude of improvement was related to pretraining (baseline) proprioceptive sense, indicating that those who initially had better lower limb position sense showed greater changes. Participants also showed improvements in performance of a precision stepping task. NEW & NOTEWORTHY We show that it is possible to alter proprioceptive sense in people with incomplete SCI using a passive proprioception training protocol combined with feedback. Improvements in proprioceptive sense transferred from end-point to joint position sense and also to an untrained precision stepping task.
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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) | 0.001 | 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) | 0.003 | 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".