Birth weight and prepubertal body size predict menarcheal age in India, Peru, and Vietnam
Bibliographic record
Abstract
Evidence on the associations of birth weight and prepubertal nutritional status with menarcheal age for low- and middle-income countries is limited. We investigated these relationships using the Young Lives younger cohort for 2001 Indian, Peruvian, and Vietnamese girls born in 2001-2002. Girls were followed at approximately ages 1, 5, 8, and 12 years. Weibull survival models estimated hazards of earlier menarche on the basis of birth weight Z-scores (BWZ), and age-8 BMI-for-age Z-scores (BMIZ) and height-for-age Z-scores (HAZ). Estimates controlled for potential individual-, mother-, and household-level confounders and for changes in anthropometry between 1 and 8 years. In adjusted models, BWZ predicted later age at menarche (hazard ratio (HR) = 0.90, 95% CI: 0.83-0.97). Conversely, HAZ (HR = 1.66, 95% CI 1.5-1.83) and BMIZ at 8 years (HR = 1.28, 95% CI: 1.18-1.38) predicted earlier menarche. Changes in HAZ and BMIZ between 1 and 8 years were not associated with earlier menarche. Associations were consistent across countries, though with variation in estimated magnitudes. Maternal height and age were associated with later menarche. This evidence points to consistently robust and opposite associations of birth weight versus prepubertal attained height and body mass index with menarcheal age in three diverse settings with regard to nutrition, ethnicity, and socioeconomic status.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".