Using the Berg Balance Scale to Distinguish Balance Abilities in Children with Cerebral Palsy
Bibliographic record
Abstract
PURPOSE: This study was designed to evaluate the use of the Berg Balance Scale (BBS) to assess the balance abilities of children with cerebral palsy. METHODS: Thirty-six ambulatory children with cerebral palsy and 14 children with no motor impairment (ages eight to 12 years) were assessed on the BBS and the Gross Motor Function Measure (GMFM). Participants with cerebral palsy comprised three groups based on diagnosis (spastic hemiplegia, spastic diplegia who ambulated without aids, and spastic diplegia who ambulated with aids). A fourth group consisted of control subjects with no motor impairment. It was hypothesized that these four groups demonstrated a hierarchy of balance abilities. A one-way ANOVA was used to detect significant differences in test scores among the four groups. The analysis was repeated categorizing children on the Gross Motor Function Classification System (GMFCS) instead of diagnosis. RESULTS: The ability to use scores on the GMFM was slightly better than using BBS scores to distinguish among the groups when children were classified using diagnosis. Significant pair-wise differences among the groups were present on both the BBS and the GMFM when the children were grouped on the GMFCS. CONCLUSIONS: The results suggest that the BBS can be considered as a clinical measure of balance for children with cerebral palsy, and a functional classification system can be used to group children more homogeneously than traditional classification by diagnosis.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".