Body fat accrual trajectories for a sample of Asian‐Canadian and Caucasian‐Canadian children and youth: A longitudinal DXA‐based study
Bibliographic record
Abstract
BACKGROUND: Body fat accrual trajectories can be used to monitor trends in body fat mass and identify potential health risks. Currently, no body fat percent (BF%) centile distance and velocity curves exist for Canadian youth. OBJECTIVES: To develop sex-specific and ethnic-specific BF% centile distance and velocity curves for White and Asian-Canadian youth. METHODS: We utilized 4734 observations from 944 participants (female = 487; Asian = 532) to create sex-specific BF% velocity curves for age 10 to 18 years and sex-specific and ethnic-specific BF% percentile distance curves for ages 9 to 18 years for White and Asian children. BF% was derived from whole body DXA scans. RESULTS: BF% centile distance curves for Asian and White girls were similar. BF% at most centiles plateaued around age 16 for Asian but not for White boys. Velocity curves for boys declined from age 11 to 13 years and then increased until age 18 years. For girls from 10 to 15 years, velocity curves converged towards the 50th centile then remained flat from 16 to 18 years. CONCLUSIONS: BF% distance and velocity centiles can be used to identify when an individual veers from an average BF% accrual trajectory. In future, these curves may be used to investigate differences in fat mass and accrual across Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".