Effect of forced use therapy on posture in children with hemiplegic cerebral palsy: A pilot study
Bibliographic record
Abstract
OBJECTIVE: To evaluate the impact of forced use therapy on posture in children with hemiplegic cerebral palsy. DESIGN: Single group pre- and post-training assessments. SUBJECTS: Eight children (mean age 10.5 years (standard deviation 1.26 years)) with hemiplegic cerebral palsy Levels I and II on the gross motor function classification scale. METHODS: All participants underwent 12 days (6 h/day) of forced use therapy. Postural asymmetry as well as the centre of pressure range and peak velocity during quiet standing were evaluated before and after the therapy. Upper limb functional level was also assessed using the Bruininks Oseretsky test of Motor Proficiency and the Assisting Hand Assessment. RESULTS: Before forced use therapy, postural asymmetry tended to decrease when the participants wore the upper limb constraint. After forced use therapy, upper limb functional scores improved significantly, and postural asymmetry tended to decrease, compared with the pre-therapy values. Postural improvement was correlated with postural asymmetry before forced use therapy. No significant differences were observed on the centre of pressure displacement parameters during the quiet standing tests in all conditions. CONCLUSION: This pilot study showed that forced use therapy may be an efficient way to improve postural asymmetry in children with hemiplegic cerebral palsy.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".