Maternal Pre-Pregnancy BMI and Gestational Weight Gain Are Associated with Preschool Children’s Neuropsychological Outcomes in the APrON Cohort
Bibliographic record
Abstract
This study examined the associations between maternal pre-pregnancy BMI and gestational weight gain (GWG) and children’s neuropsychological outcomes at 3 to 5 years of age. A total of 379 women and their children from the Alberta Pregnancy Outcomes and Nutrition (APrON) study participated. Covariate-adjusted robust regressions examined associations between maternal pre-pregnancy BMI, GWG class, interaction terms, and child outcomes. Each unit increase in maternal BMI was linked to a 0.48-point decrement (95% CI: −0.75 to −0.21) in children’s Full Scale IQ. Higher pre-pregnancy BMI was related to poorer performance on the other intelligence indexes (B = −0.35 to −0.47, 95% CIs: −0.75, −0.02) and lower performance on measures of language (B = −0.08 to −0.09, 95% CIs: −0.16, −0.02), motor skills (B = −0.08 to −0.11, 95% CIs: −0.18, −0.01), and executive function (B = −0.09 to −0.16, 95% CIs: −0.26, −0.01). GWG below the recommended range was associated with a 4.04-point decrement (95% CI: 7.89, −0.11) in Full Scale IQ, but better performance on a spatial working memory test (B = 0.27, 95% CI: 0.02, 0.52). GWG above the recommended range was associated with lower language (B = −0.79, 95% CI: −1.52, −0.06) and memory scores (B = −0.93, 95% CI: −1.64, −0.22). Interactions were found between pre-pregnancy BMI and GWG on measures of intelligence and executive function. Maternal pre-pregnancy BMI and GWG are related to children’s performance in various neuropsychological domains and may interact to predict outcomes. Optimizing maternal health and weight prior to conception and during pregnancy may enhance children’s neuropsychological outcomes.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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".