Stretching with Children with Cerebral Palsy: What Do We Know and Where Are We Going?
Bibliographic record
Abstract
In Brief Purpose: To review research regarding mechanisms of muscle contracture in cerebral palsy (CP) and the effectiveness of stretching, and to discuss current physical therapy stretching practices. Community-based recreation opportunities that encourage flexibility and fitness are explored as alternatives to traditional therapy stretching approaches. Summary of Key Points: Mechanisms of muscle contracture in children with CP are unclear and clinical research evaluating the effects of stretching is inconclusive. Recent shifts in thinking about the management of children with CP suggest an increased emphasis on flexibility, fitness, and participation in activities that are meaningful to children and families. Statement of Conclusions: Additional research is needed to explore the structural changes that occur in the shortened muscles of children with CP and the effects of stretching practices used in pediatric physical therapy. Recommendations for Clinical Practice: Physical therapists can consider innovative alternatives that integrate flexibility and fitness goals with community-based recreation programs. After a review of research on muscle contracture in cerebral palsy and the effectiveness of stretching, the authors discuss current physical therapy stretching practices and suggest that community-based fitness opportunities that encourage flexibility and fitness may be an alternative to traditional stretching approaches.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".