Differences in maternal smoking across successive pregnancies – dose‐dependent relation to BMI<i>z</i>‐score in the offspring: an individual patient data (IPD) meta‐analysis
Bibliographic record
Abstract
INTRODUCTION: Uncontrolled family factors may bias the estimation of the association between maternal smoking during pregnancy and offspring body mass index (BMI). The objective was to assess if there is an association between maternal smoking during pregnancy and offspring BMI z-score independent of factors in the siblings' shared environment and if such association is linear. METHODS: We performed an individual patient data meta-analysis using five studies providing sibling data (45,299 children from 14,231 families). In a multi-level model, separating within-family and between-family effects and with random intercept for families, we analysed the dose-response association between maternal number of cigarettes per day during pregnancy and offspring's BMI z-score using B-splines to allow for non-linear associations. RESULTS: A linear within-family effect for number of cigarettes smoked in the range from 1 to 30 cigarettes per day on the offspring's BMI z-score was observed. Each additional cigarette per day between sibling pregnancies resulted in an increase in BMI z-score of 0.007 (95% CI [0.006, 0.009]). A between family-effect emerged only with doses ≥25 cigarettes per day. CONCLUSIONS: The number of cigarettes mothers smoke per day during pregnancy is positively associated with offspring BMI z-score even among siblings, suggesting that the association is not entirely explained by confounding by family factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".