Training intensity of robot‐assisted gait training in children with cerebral palsy
Bibliographic record
Abstract
AIM: We compared three different intensities of robot-assisted gait training (RAGT) for achieving favourable outcomes in children with cerebral palsy (CP). METHOD: This study was conducted using a randomized controlled, single-blind design. Thirty children (19 males and 11 females; mean age 6 years 1 month, SD 2 years) with CP classified in Gross Motor Function Classification System levels II and III were assigned to three different RAGT intensity groups: high-intensity (fastest walking speed and lowest body weight support [BWS]), low-intensity (slowest speed and highest BWS), and comfortable intensity (intermediate speed and intermediate BWS). The RAGT intervention was performed three times a week for 6 weeks. Outcome measures included the 88-item Gross Motor Function Measure, stability index, spatiotemporal parameters of gait analysis, paediatric functional independence measure, and the Canadian Occupational Performance Measure. RESULTS: The 88-item Gross Motor Function Measure was significantly improved after training in the high-intensity (D Δ8.3 ± 15.6; E Δ3.8 ± 4.1) and comfortable intensity (D Δ2.9 ± 3.1; E Δ1.2 ± 2.0) groups, whereas gait speed was improved in the comfortable intensity group, without statistically significant group differences. Only the low-intensity group showed improvement on the stability index (Δ -0.6 ± 0.9, p = 0.05). Everyday functional performance significantly improved in all three groups, with the comfortable intensity group showing the greatest improvement. INTERPRETATION: Different training intensities produced improvement in different areas; individualized RAGT intensity adjustment is therefore needed based on the rehabilitation goal.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".