Robotic Mechanotherapy in the Comprehensive Rehabilitation of Children with Cerebral Palsy in the Early Period after Selective Dorsal Rhizotomy: a Prospective Non-Randomized Study
Bibliographic record
Abstract
Background. Mechanotherapy is an effective method of rehabilitation for patients with cerebral palsy (CP), but its potential use after selective dorsal rhizotomy (SDR) is poorly understood. The aim of the study is to evaluate the feasibility and impact of robotic mechanotherapy methods on the motor skills of patients with spinal cord injury in the first months after surgery. Methods. A prospective, non-randomized, uncontrolled, single-center study was conducted involving 22 children with bilateral spastic forms of cerebral palsy aged 3.5 to 16.9 years (median age: 7.2 years) who had undergone SDR 7 to 25 days (median: 8 days) before the start of rehabilitation. Based on an examination by a multidisciplinary team of specialists, in addition to physical and physiotherapeutic rehabilitation methods, the patients received 10 mechanotherapy procedures: Galileo vibration platform for 22 (100%) patients, Motek C-mill sensory treadmill with support system and biofeedback for 12 (54.5%) children, and EA Bambini exoskeleton for 8 (36.4%) patients. Results. During the analyzed period of time, patients who used the sensory track showed statistically significant improvements in their gait parameters: an increase in the length of their steps with both their right and left legs, a more even distribution of weight on their limbs, and an increase in their walking distance and number of steps per minute. Patients who received training with an exoskeleton showed a significant increase in their training time and walking distance, as well as an increase in their walking speed and total number of steps per training session, and an increase in their voluntary activity on the simulator. The main limiting factors for the usage of these methods were pain syndrome, cognitive impairments, and the psychoemotional state of children after surgery. Conclusion. The use of robotic mechanotherapy methods can help improve patients’ endurance, functional skills, and activity in the early post-SDR period, but the selection of specific methods should take into account individual patient limitations and the time elapsed since surgery.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".