Child academic achievement in association with pre-pregnancy obesity and gestational weight gain
Bibliographic record
Abstract
BACKGROUND: Recent data suggest that children of mothers who are obese before pregnancy, or who gain too much weight during pregnancy, may be at an increased risk of cognitive impairments. METHODS: Mother-infant dyads enrolled in a birth cohort study in Pittsburgh, Pennsylvania (1983-1986), were followed from early pregnancy to 14 years postpartum (n=574). Math, reading and spelling achievements were assessed at ages 6 and 10 years using the Wide Range Achievement Test-Revised, and at age 14 years using the Wechsler Individual Achievement Test Screener. Self-reported total GWG was converted to gestational age-standardised z-scores. Generalised estimating equations were used to estimate the effects of GWG and pre-pregnancy body mass index (BMI) on academic achievement at 6, 10 and 14 years, while adjusting for maternal race, child sex, parity, employment, family income, maternal intelligence, maternal depression, pre-pregnancy BMI (in GWG models only) and the home environment. RESULTS: The mean (SD) BMI was 23.4 (5.7) kg/m(2) and the mean (SD) GWG reported at delivery was 14.4 (5.9) kg. There was a significant non-linear association between pre-pregnancy BMI and an offspring's academic achievement. At 6, 10 and 14 years, an offspring's academic scores were inversely associated with pre-pregnancy BMI beyond 22 kg/m(2). High GWG (>1 SD) was associated with approximately 4-point lower reading (adjusted β (adjβ) -3.75, 95% CI -7.1 to -0.4) and spelling scores (adjβ -3.90, 95% CI -7.8 to -0.2), compared with GWG -1 to +1 SD. CONCLUSIONS: Future studies in larger and socioeconomically diverse populations are needed to confirm maternal weight and weight gain as causal determinants of a child's academic skills, and whether this effect persists into adulthood.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".