Quality of Movement Changes in Ambulatory Children with Cerebral Palsy 1 Year after Selective Dorsal Rhizotomy
Bibliographic record
Abstract
Purpose: Quality of movement is an important component of effective gross motor performance in children with cerebral palsy. This study used the Quality Function Measure (QFM) to evaluate changes in quality of movement in ambulatory children with cerebral palsy 1 year after selective dorsal rhizotomy (SDR). Method: In a retrospective study, using a single-group repeated measure design, 48 children (28 male) at preoperative Gross Motor Function Classification System (GMFCS) level II ( n = 18) and III ( n = 30) were evaluated. Mean (standard deviation) age at surgery was 7 years 11 months (2 years 3 months). Scores for all five QFM attributes, Alignment, Coordination, Dissociated Movements, Stability and Weight-shift were obtained. Results: All QFM attributes improved significantly ( p < 0.001) for all children and within both GMFCS levels. Mean (SD) change in QFM scores varied from 9.02% (8.61) for Stability to 26.5% (22.06) for Alignment. Conclusions: SDR and subsequent rehabilitation are associated with an improvement in the quality of gross motor movement in children with cerebral palsy. This study provides clinicians and families with further evidence supporting the value of SDR in this population. Further studies need to evaluate the impact of improved quality of movement on a child’s function and participation in the real world.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".