Body composition measurement in young children using quantitative magnetic resonance: a comparison with air displacement plethysmography
Bibliographic record
Abstract
Summary Background Quantitative magnetic resonance (QMR) has been increasingly used to measure human body composition, but its use and validation in children is limited. Objective We compared body composition measurement by QMR and air displacement plethysmography (ADP) in preschool children from Singapore's multi‐ethnic Asian population (n = 152; mean ± SD age: 5.0 ± 0.1 years). Methods Agreements between QMR‐based and ADP‐based fat mass and fat mass index (FMI) were assessed using intraclass correlation coefficient (ICC), reduced major axis regression and Bland–Altman plot analyses. Analyses were stratified for the child's sex. Results Substantial agreement was observed between QMR‐based and ADP‐based fat mass (ICC: 0.85) and FMI (ICC: 0.82). Reduced major axis regression analysis suggested that QMR measurements were generally lower than ADP measurements. Bland–Altman analysis similarly revealed that QMR‐based fat mass were (mean difference [95% limits of agreement]) −0.5 (−2.1 to +1.1) kg lower than ADP‐based fat mass and QMR‐based FMI were −0.4 (−1.8 to +0.9) kg/m2 lower than ADP‐based FMI. Stratification by offspring sex revealed better agreement of QMR and ADP measurements in girls than in boys. Conclusions QMR‐based fat mass and FMI showed substantial agreement with, but was generally lower than, ADP‐based measures in young Asian 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 | 0.003 | 0.005 |
| 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".