A Narrative Review on Robotic-Assisted Gait Training in Children and Adolescents with Cerebral Palsy: Training Parameters, Choice of Settings, and Perspectives
Bibliographic record
Abstract
About 70% of children and adolescents with cerebral palsy experience gait impairments which affect their autonomy and well-being. Robotic-assisted gait training using the Lokomat is particularly promising for rehabilitation as it provides a standardized environment favoring the massive repetition of the movement, in which physical demands are low on the therapist and high training loads can be achieved. As no guidelines exist regarding training protocols and Lokomat settings, the goal of this narrative review was to summarize previously published information on the use of RAGT in children and adolescents with cerebral palsy and to provide an opinion on possibilities for improving future research. The thirteen studies reviewed reported both positive and null effects of Lokomat training on gait. Half of the studies combined the Lokomat with other types of training, and only five used a control intervention to assess its benefit. Overall, training was administered 1–5 times per week for 20–60 min, over 1–12 weeks. Although Lokomat settings were not always described, progressively decreasing body weight support and guidance while increasing the treadmill speed appeared to be prioritized. The variety of training protocols and settings used did not allow pooling of the studies to assess the effects of interventions on gait parameters in children and adolescents with cerebral palsy. This narrative review highlights the need for homogenization of interventions so that clear guidelines can emerge and be applied in rehabilitation centers.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".