Normative Data and Predictors of Leg Muscle Function and Postural Control in Children
Bibliographic record
Abstract
INTRODUCTION: At the present there are limited tools available to measure muscle function in young children. Ground reaction force plates measure lower-body function and postural control in older children and adults. The purpose of this study was threefold: 1) develop normative data for evaluating global muscle development; 2) determine the reproducibility of ground reaction force plates for assessing muscle function in preschool-age children; and 3) identify predictors of skeletal muscle function. METHODS: Children's (n = 81, 1.8 to 6.0 yr; M = 52%) muscle function and postural control was measured for jump (JMP), sit-to-stand (STS), and both undistracted and distracted body sway tests using a ground reaction force plate (Kistler 9200A). Whole body composition used dual-energy x-ray absorptiometry (Hologic 4500A Discovery Series). Plasma 25-hydroxyvitamin D [25(OH)D] and parathyroid hormone concentrations were measured by chemiluminescence (Liaison, Diasorin, Mississauga, ON, Canada) as well as ionized calcium (ABL80 FLEX, Radiometer Medical A/S). Demographics, and anthropometry were collected. ANOVA and linear regression were used to identify predictors. Reproducibility was assessed by intersubject coefficient of variation. RESULTS: Age was a consistent predictor in all models; body size or fat and lean mass were important predictors in 3 of the models - STS peak force, STS peak power, and JMP peak power. STS was the most reproducible maneuver (average coefficient of variation =15.7%). Distracted body sway testing was not appropriate in these youngsters. CONCLUSION: The novel data presented in this study demonstrate a clear age (developmental) effect without any effect of sex on muscle function and postural control in young children. Lean muscle mass was important in some models (STS peak force and JMP peak power). The STS test was the best of the 4 maneuvers.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".