Components of Standing Postural Control Evaluated in Pediatric Balance Measures: A Scoping Review
Bibliographic record
Abstract
OBJECTIVE: To identify measures of standing balance validated in pediatric populations, and to determine the components of postural control captured in each tool. DATA SOURCES: Electronic searches of MEDLINE, Embase, and CINAHL databases using key word combinations of postural balance/equilibrium, psychometrics/reproducibility of results/predictive value of tests, and child/pediatrics; gray literature; and hand searches. STUDY SELECTION: Inclusion criteria were measures with a stated objective to assess balance, with pediatric (≤18y) populations, with at least 1 psychometric evaluation, with at least 1 standing task, with a standardized protocol and evaluation criteria, and published in English. Two reviewers independently identified studies for inclusion. There were 21 measures included. DATA EXTRACTION: Two reviewers extracted descriptive characteristics, and 2 investigators independently coded components of balance in each measure using a systems perspective for postural control, an established framework for balance in pediatric populations. DATA SYNTHESIS: Components of balance evaluated in measures were underlying motor systems (100% of measures), anticipatory postural control (72%), static stability (62%), sensory integration (52%), dynamic stability (48%), functional stability limits (24%), cognitive influences (24%), verticality (9%), and reactive postural control (0%). CONCLUSIONS: Assessing children's balance with valid and comprehensive measures is important for ensuring development of safe mobility and independence with functional tasks. Balance measures validated in pediatric populations to date do not comprehensively assess standing postural control and omit some key components for safe mobility and independence. Existing balance measures, that have been validated in adult populations and address some of the existing gaps in pediatric measures, warrant consideration for validation in children.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".