Diet Quality Is Associated with a High Newborn Size and Reduction in the Risk of Low Birth Weight and Small for Gestational Age in a Group of Mexican Pregnant Women: An Observational Study
Bibliographic record
Abstract
A high-quality diet during pregnancy may have positive effects on fetal growth and nutritional status at birth, and it may modify the risk of developing chronic diseases later in life. The aim of this study was to evaluate the association between diet quality and newborn nutritional status in a group of pregnant Mexican women. As part of the ongoing Mexican prospective cohort study, OBESO, we studied 226 healthy pregnant women. We adapted the Alternated Healthy Eating Index-2010 for pregnancy (AHEI-10P). The association between maternal diet and newborn nutritional status was investigated by multiple linear regression and logistic regression models. We applied three 24-h recalls during the second half of gestation. As the AHEI-10P score improved by 5 units, the birth weight and length increased (β = 74.8 ± 35.0 g and β = 0.3 ± 0.4 cm, respectively, p < 0.05). Similarly, the risk of low birth weight (LBW) and small for gestational age (SGA) decreased (OR: 0.47, 95%CI: 0.27–0.82 and OR: 0.55, 95%CI: 0.36–0.85, respectively). In women without preeclampsia and/or GDM, the risk of stunting decreased as the diet quality score increased (+5 units) (OR: 0.62, 95%IC: 0.40–0.96). A high-quality diet during pregnancy was associated with a higher newborn size and a reduced risk of LBW and SGA in this group of pregnant Mexican women.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".