Maternal pre‐pregnancy body mass index, gestational weight gain and child weight during the first 2 years of life in an Amazonian birth cohort
Bibliographic record
Abstract
BACKGROUND: In socially vulnerable populations, evidence is needed regarding the role of maternal nutritional status on child weight during the first 2 years of life. OBJECTIVES: We aimed to assess the association of pre-pregnancy body mass index (BMI) and gestational weight gain (GWG) with offspring BMI-for-age z-scores (BAZs) during the first 2 years of life. METHODS: A population-based birth cohort study was conducted with 900 mother-child pairs. Pre-pregnancy weight and weight at delivery were collected from medical records, and anthropometric data were measured at birth and at 6-month, 1-year and 2-year follow-up visits. Linear regression and linear mixed-effect models assessed associations with pre-pregnancy BMI, GWG and BAZ during the first 2 years of life. RESULTS: Pre-pregnancy overweight and obesity and excessive GWG were positively associated with BAZ at birth and at 1- and 2-year follow-up visits. There were no significant additional BAZ changes per year based on the exposures up to age 2 years. CONCLUSIONS: Elevated maternal pre-pregnancy BMI and GWG were associated with a child's higher BAZ at birth, and these differences remained constant throughout the first 2 years of life in Amazonian children. These findings highlight the importance of promoting adequate maternal weight before pregnancy and during prenatal care also in socially vulnerable populations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".