An overview of systematic reviews of adaptive seating interventions for children with cerebral palsy: where do we go from here?
Bibliographic record
Abstract
Purpose: This article provides an overview and assessment of systematic reviews of adaptive seating outcomes in children with cerebral palsy (CP) and proposes strategies to advance and improve the clinical utility of future research evidence. Methods: A comprehensive search for systematic reviews was performed to locate and evaluate the functional effects of adaptive seating in children with CP. Relevant electronic databases were searched to identify reviews published between January 1990 and December 2010. Results: The search yielded five reviews of adaptive seating interventions that found generally positive, but inconclusive evidence of effectiveness for postural control and management, seated posture, upper extremity function, and overall clinical outcomes. Review authors consistently reported that they were unable to combine data from original research studies to make sound clinical recommendations due to the low quality of studies, the lack of appropriate outcome indicators, and the heterogeneity and lack of clarity in population characteristics and adaptive seating interventions. Conclusions: Strategies to improve the quality and clinical relevance of new research evidence for adaptive seating interventions include the adoption of an expanded view of child functioning, a child motor function classification and development approach, and contemporary frameworks for the measurement and evaluation of assistive technology outcomes.Implications for RehabilitationAssistive technology practitioners recommend adaptive seating devices to improve child functioning.Past systematic reviews of adaptive seating outcomes in children with cerebral palsy provide limited empirical support.Current thinking about child health and assistive technology outcomes provides direction to inform research and clinical practice.
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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 | 0.019 | 0.085 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".