Early childhood weight gain: Latent patterns and body composition outcomes
Bibliographic record
Abstract
BACKGROUND: Despite early childhood weight gain being a key indicator of obesity risk, we do not have a good understanding of the different patterns that exist. OBJECTIVES: To identify and characterise distinct groups of children displaying similar early-life weight trajectories. METHODS: A growth mixture model captured heterogeneity in weight trajectories between 0 and 60 months in 1390 children in the Avon Longitudinal Study of Parents and Children. Differences between the classes in characteristics and body size/composition at 9 years were investigated. RESULTS: The best model had five classes. The "Normal" (45%) and "Normal after initial catch-down" (24%) classes were close to the 50th centile of a growth standard between 24 and 60 months. The "High-decreasing" (21%) and "Stable-high" (7%) classes peaked at the ~91st centile at 12-18 months, but while the former declined to the ~75th centile and comprised constitutionally big children, the latter did not. The "Rapidly increasing" (3%) class gained weight from below the 50th centile at 4 months to above the 91st centile at 60 months. By 9 years, their mean body mass index (BMI) placed them at the 98th centile. This class was characterised by the highest maternal BMI; highest parity; highest levels of gestational hypertension and diabetes; and the lowest socio-economic position. At 9 years, the "Rapidly increasing" class was estimated to have 68.2% (95% confidence interval [CI] 48.3, 88.1) more fat mass than the "Normal" class, but only 14.0% (95% CI 9.1, 18.9) more lean mass. CONCLUSIONS: Criteria used in growth monitoring practice are unlikely to consistently distinguish between the different patterns of weight gain reported here.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".