Changes in the Sensory Weighting Strategies in Balance Control Throughout Maturation in Children
Bibliographic record
Abstract
Abstract Background The central nervous system integrates information from different sensory inputs (vestibular, visual, and somatosensory) to maintain balance. However, strategies for weighing sensory information change as maturation occurs. Purpose The purpose of this study was to: (1) evaluate postural control development in a large sample of healthy children aged 5 to 17 years old, (2) analyze changes in sensory weighting strategies as maturation occurs, and (3) determine the extent to which anthropometric characteristics (height, weight, body mass index [BMI]) influence postural control. Sample Size This study recruited 120 healthy children, equally distributed in gender and number, into four age groups (5‐8 years, 9‐11 years, 12‐14 years, and 15‐17 years) and compared them to a control group of 20 healthy adults (aged 20‐25 years). Research Design The sensory organization test (SOT) was used to assess overall balance and the use of specific sensory inputs to maintain postural control. All children underwent the six SOT conditions: (1) eyes open, surround and platform stable, (2) eyes closed, surround and platform stable, (3) eyes open, sway-referenced surround, platform stable, (4) eyes open, sway-referenced platform, (5) eyes closed, sway-referenced platform, and (6) eyes open, sway-referenced surround and platform. Data Analysis Condition-specific equilibrium scores (ES), composite equilibrium scores (CES), and sensory analysis ratios were analyzed to determine whether the performance was related to age, gender, or specific anthropometric characteristics (height, weight, and BMI). Results Data showed a significant age-associated improvement in ES for all 6 conditions ( p < 0.05) and in CES ( p = 0.001). For both genders, (1) somatosensory function was adult-like by age 5 to 8 years, (2) visual function peaked around age 12 years, and (3) vestibular function reached maturity by age 15 to 17 years ( p < 0.05). A moderate positive correlation ( r (140) = 0.684, p = 0.01; two-tailed) between height and CES was found and a weak positive correlation ( r (140) = 0.198, p = 0.01) between height and somatosensory ratio was noted. Lower vestibular ratio scores were observed in children who had a higher BMI ( p = 0.001). Conclusion The efficient use of individual sensory system input to maintain balance does not occur at the same age. Age and gender affect the changes in sensory weighting strategies, while height and BMI influence postural control in children. These factors need to be accounted for in child assessment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".