Repetitive Mass Practice or Focused Precise Practice for Retraining Walking After Incomplete Spinal Cord Injury? A Pilot Randomized Clinical Trial
Bibliographic record
Abstract
BACKGROUND: Retraining walking following spinal cord injury using visually guided tasks may be especially efficacious because it engages the motor cortex, whose input may facilitate improvements in functional walking. OBJECTIVES: To contrast 2 methods of retraining, one emphasizing precise, visually guided walking over obstacles and on targets (Precision Training), the other emphasizing mass practice of walking on a treadmill (Endurance Training). METHODS: A randomized, single-blind, crossover design was used. Twenty-two participants, ≥7 months postinjury, were randomly allocated to start with Precision or Endurance Training. Each phase of training was 5 times per week for 2 months, followed by a 2-month rest. MEASURES: of walking speed, distance, skill, confidence, and depression were obtained before training, then monthly thereafter. RESULTS: Both forms of training led to significant improvements in walking, with Endurance Training inducing bigger improvements in walking distance than Precision Training, especially for high-functioning walkers who had initial walking speeds >0.5 m/s. The largest improvements in walking speed and distance occurred in the first month of Endurance Training, with minimal changes in the second month of training. In contrast, improvements in walking skill occurred over both months during both types of training. Retention of over ground walking speed, distance, and skill was excellent for both types of training. CONCLUSIONS: Intensive walking training in the chronic phase after spinal cord injury is effective in improving over ground walking. Visually guided tasks for training individuals with chronic spinal cord injury were not superior to mass practice on a treadmill.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".