Early life growth trajectories and future risk for overweight
Bibliographic record
Abstract
Using a newly developed statistical technique, our objective was to test whether key aspects of growth trajectories between 0–24 months of age, such as size, velocity, and timing of peak velocity, were independently associated with overweight in late childhood. We used data from a birth cohort in Mexico (n=586) and the SuperImposition by Translation and Rotation (SITAR) method to estimate infant/childhood (0–24 months) growth trajectories for BMI, height, and weight. The SITAR method uses a nonlinear random‐effects model to estimate the average growth curve and each individual's deviation from this average curve on three dimensions—size, velocity, and timing of peak velocity. In separate models for each of the anthropometric measures (BMI, height, and weight), logistic regression estimated the association between overweight in late childhood and size, velocity, and timing of the BMI/height/weight trajectory. In crude models, relative BMI/height/weight (size) and BMI/height/weight velocity during 0–24 months were each associated with increased odds of overweight. In the mutually‐adjusted models, only relative BMI/height/weight (size), but not velocity, remained statistically significant. We found no evidence that the association between velocity and overweight varied by size. These results are not consistent with the hypothesis that growth velocity in infancy programs future risk for overweight. Grant Funding Source : This work was supported by pilot funds from the Berkeley Population Center NICHD R21 (HD056581).
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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 | 0.006 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".