Effectiveness of Adaptive Seating on Sitting Posture and Postural Control in Children with Cerebral Palsy
Bibliographic record
Abstract
In Brief Purpose: We reviewed research on the effect of adaptive seating on sitting posture/postural control in children with cerebral palsy. Second, we examined whether changes in postural control related to changes in other aspects of functioning. Methods: Electronic database/hand searches were undertaken to locate studies published in English. Reviewers screened studies for inclusion criteria, extracted data, indexed outcomes to the International Classification of Functioning, Disability and Health, assigned levels of evidence, and assessed study quality. Results: Thirteen of 14 articles used group designs and the other a single-subject design. Conflicting findings were reported for saddle seats and optimal seat/back angle for improving sitting posture/postural control. Significant improvements were reported with seat inserts, external supports, and modular seating systems. Evidence supporting effects of postural control on functional abilities was limited. Conclusions: Future studies on the effects of adaptive seating should describe participants with standardized classification systems and employ stronger research designs. These authors note that findings reported in the literature are conflicting for saddle seats and the optimal seating angles for improving sitting posture. Significant improvements were reported with seat inserts, external supports, and modular seating systems, but effects on function were limited.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".