Gait Retraining After Neurological Disorders
Bibliographic record
Abstract
Abstract Neurological disorders like traumatic brain injury, stroke, and spinal cord injury often adversely affect a person's ability to walk. Restoration of gait function is an important goal in the rehabilitation of these patients. Appropriate motor training can facilitate adaptations in the nervous system to recover function after an injury. Important aspects of motor training include task‐specific performance and repetition of the affected movement. This means that to improve walking, the patient must walk. Body‐weight supported treadmill training is becoming increasingly accepted as a valuable strategy for gait rehabilitation after neurological injury. Treadmills that run at slow locomotor speeds allow patients who are partially unloaded from their body weight to walk, possibly with manual assistance by one or more therapists. However, manual therapy is limited in terms of duration, repeatability, and quantification. Robotic devices allow longer training sessions with better support and control of the leg movements. Current developments are directed to patient‐cooperative strategies, which allow the robotic device to adapt to the patient's needs and efforts. Furthermore, proper design of these devices leaves open many possibilities for a repertoire of additional assessment tools that measure patients’ performance during training and other clinical parameters. Future directions in this field include combining robotic devices with functional electrical stimulation and the development of robotic orthoses for retraining overground walking.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".