Developmental pathways leading to obesity in childhood.
Bibliographic record
Abstract
BACKGROUND: Researchers have yet to provide a comprehensive explanation of the variability in the development of childhood obesity, owing in part to the dearth of longitudinal studies. Such an understanding would contribute to the improvement of approaches for the primary and secondary prevention of childhood obesity. This study identifies, in a representative sample of Canadian children, age-related patterns of overweight and obesity between toddlerhood and childhood. DATA AND METHODS: The data are from cycles 2 through 5 (1996/1997 to 2002/2003) of the National Longitudinal Survey of Children and Youth. The sample comprised children aged 24 to 35 months at baseline, who were followed biennially over six years. Group-based mixture modelling analyses (using SAS PROC TRAJ) were conducted to identify the sex-specific developmental trajectories of body mass index (BMI). RESULTS: Group-based modelling identified four BMI trajectories for the girls (stable normal BMI, early declining BMI, late declining BMI, and an accelerating rise to obesity) and three for the boys (stable normal BMI, transient high BMI, and a J-curve rise to obesity). INTERPRETATION: Identifying distinct, sex-specific BMI trajectories is valuable in understanding pathways through which a child may develop obesity. These findings have implications for further research and practice, in particular, that no single approach can be used to prevent or reduce levels of obesity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".