Age‐ and gender‐dependent values of skeletal muscle mass in healthy children and adolescents
Bibliographic record
Abstract
BACKGROUND: Skeletal muscle mass (SMM) can be extracted from whole-body scans obtained by X-ray-based dual-photon absorptiometry (DXA). There is a need to establish expected age-dependent values for children and adolescents. METHODS: Appendicular lean tissue mass (ALM) was extracted from whole-body DXA scans in 140 healthy children and adolescents (68 females and 72 males). Whole-body SMM was calculated from ALM using equations developed by Kim et al. (Am J Clin Nutr 84:1014-1020, 2006). Age-dependent patterns of increase in SMM were derived by fitting SMM values to equations that consisted of the sum of two logistic expressions, one accounting for SMM changes during growth and the other for SMM changes during puberty. Normal ranges were defined so that 95% of the SMM values were included. The reproducibility of SMM measurements was obtained from whole-body DXA scans repeated on three occasions in each of a separate group of 32 normal children with repositioning between scans. RESULTS: Normal ranges are presented as equations describing the age-dependent pattern of increase in SMM as well as population standard deviations that increased steadily with age. For 15 children below age 10, SMM reproducibility (95% CI) was 149 g (119-199 g) while for 17 children and adolescents over age 10, reproducibility was 170 g (138-223 g). CONCLUSION: DXA-based measurements of SMM in children and adolescents are reproducible and can be expressed in terms of age-dependent Z scores.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".