RObotic WAlking for children who CAnnot WAlk (RoWaCaWa): Feasibility and family impacts and perspectives of a family-led Intervention
Bibliographic record
Abstract
Abstract Purpose To evaluate the feasibility, impacts, and perspectives of a family-led robotic walking intervention. Materials & Methods This single-arm interventional study recruited participants aged ≥4 years with pediatric-onset neuromotor disorders. Participants were lent a robotic walker and recommended to use at least 150min/week for 12-weeks. Robotic walking use, acceptability, practicality and adverse events were tracked. Family goals were measured before and after training period using Canadian Occupational Performance Measure (COPM). Quality of life was examined using EQ-5D-Y, Carer-QoL, and CP-CHILD. Quantitative data were analyzed using descriptive statistics (median (25 th -75 th percentile)) and Wilcoxon signed-rank tests. Qualitative interviews captured family perspectives and were analyzed using thematic analysis. Results 15 participants aged 4-23 completed this study. Participants trained 5 (3.5-6) times for 150(82-181) minutes and took 7,544(4,640 – 9,575) steps each week. Adverse events occurred in <1% (16 minor, 1 moderate) of robotic walking sessions. Performance (3.5 (1.9-4.5), p=<0.001) and satisfaction (3.3(3.0-5.0), p=<0.001) of goals increased. Parents described positive changes in social experiences and family interactions and difficulties with the logistics of robotic walking. Conclusions This family-led robotic walking intervention resulted in improvements in individual goals, though families did struggle with some logistics of robotic walking, such as; transport and difficulties with the device.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".