Fat Mass Centile Charts for Brazilian Children and Adolescents and the Identification of the Roles of Socioeconomic Status and Physical Fitness on Fat Mass Development
Bibliographic record
Abstract
This paper presents fat mass centile charts for Brazilian youth and investigates the roles of socioeconomic status and physical fitness (PF) on fat mass (FM) development. Two northeast Brazilian samples were used: a cross-sectional sample of 3659 (1921 girls) aged 8 to 16 years and a mixed-longitudinal series of cohorts (8-10, 10-12, 12-14, 14-16 years) with 250 boys and 250 girls. A measure of somatic maturity was used as a marker of biological maturation; PF comprised agility, explosive and static strength, and aerobic capacity. Socioeconomic status was based on school attended; public or private. Slaughter's anthropometric equations were used to estimate FM. Percentile charts was constructed using the LMS method. HLM (Hierarchical Linear Model) 7 software modeled FM changes, identifying inter-individual differences and their covariates. Girls and boys had different FM percentile values at each age; FM increased nonlinearly in both girls and boys. Higher PF levels reduced FM changes across time in both sexes. Sex-specific non-linear FM references were provided representing important tools for nutritionists, pediatriciann and educators. Physical fitness levels were found to act as a protective factor in FM increases. As such, we emphasize PF importance as a putative health marker and highlight the need for its systematic development across the school years.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".