Measuring Outcomes for Children with Cerebral Palsy Who Use Gait Trainers
Bibliographic record
Abstract
Gait trainers are walking devices that provide additional trunk and pelvic support. The primary population of children using gait trainers includes children with cerebral palsy (CP) functioning at Gross Motor Function Classification System (GMFCS) levels IV and V. A recent systematic review found that evidence supporting the effectiveness of gait trainer interventions for children was primarily descriptive and insufficient to draw firm conclusions. A major limitation identified was the lack of valid, sensitive and reliable tools for measuring change in body structure and function, activity and participation outcomes. Twelve different clinical tools were identified in the systematic review and in this paper we review and discuss the evidence supporting their reliability, validity and clinical utility for use with children using gait trainers. We also describe seven additional clinical measurement tools that may be useful with this intervention and population. The Pediatric Evaluation of Disability Inventory (PEDI) rated highest across all areas at this time. Individualized outcome measures, such as the Canadian Occupational Performance Measure (COPM) and Goal Attainment Scaling and measuring user satisfaction with tools, such as the Quebec User Evaluation of Satisfaction with assistive Technology, show potential for gait trainer outcomes research. Spatiotemporal measures appear to be less useful than functional measures with this intervention and population. All tools would benefit from further development for use with children with CP functioning at GMFCS levels IV and V.
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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 | 0.012 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".