Grandmaternal prepregnancy body mass index and infant birthweight: a mediation analysis of maternal prepregnancy body mass index
Bibliographic record
Abstract
The objectives of this study were to examine the total effect of grandmaternal (G0) prepregnancy body mass index (BMI) on infant (G2) birthweight z score and to quantify the mediation role of maternal (G1) prepregnancy BMI. Data were extracted from the Nova Scotia 3G Multigenerational Cohort. The association between G0 prepregnancy BMI and G2 birthweight z score and the mediated effect by G1 prepregnancy BMI were estimated using g-computation with adjustment for confounders identified using a directed acyclic graph and accounting for intermediate confounding. A total of 20 822 G1-G2 dyads from 18 450 G0 participants were included. Relative to G0 normal weight, G0 underweight decreased mean G2 birthweight z score (-0.11; 95% CI, -0.20 to -0.030), whereas G0 overweight and obesity increased mean G2 birthweight z score (0.091 [95% CI, 0.034-0.15] and 0.22 [95% CI, 0.11-0.33], respectively). G1 prepregnancy BMI partly mediated the association, with the largest effect size observed for G0 obesity (0.11; 95% CI, 0.080-0.14). Estimates of the direct effect were close to the null. In conclusion, grandmaternal prepregnancy BMI was associated with infant birthweight z score. Maternal prepregnancy BMI partly mediated the association, suggesting that factors related to BMI may play an important role in the transmission of weight across the maternal line.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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) | 0.004 | 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".