Prenatal environmental tobacco smoke exposure and early childhood body mass index
Bibliographic record
Abstract
Maternal smoking during pregnancy is associated with increased risk of childhood overweight body mass index (BMI). Less is known about the association between prenatal secondhand tobacco smoke (SHS) exposure and childhood BMI. We followed 292 mother-child dyads from early pregnancy to 3 years of age. Prenatal tobacco smoke exposure during pregnancy was quantified using self-report and serum cotinine biomarkers. We used linear mixed models to estimate the association between tobacco smoke exposure and BMI at birth, 4 weeks, and 1, 2 and 3 years. During pregnancy, 15% of women reported SHS exposure and 12% reported active smoking, but 51% of women had cotinine levels consistent with SHS exposure and 10% had cotinine concentrations indicative of active smoking. After adjustment for confounders, children born to active smokers (self-report or serum cotinine) had higher BMI at 2 and 3 years of age, compared with unexposed children. Children born to women with prenatal serum cotinine concentrations indicative of SHS exposure had higher BMI at 2 (mean difference [MD] 0.3 [95% confidence interval -0.1, 0.7]) and 3 (MD 0.4 [0, 0.8]) years compared with unexposed children. Using self-reported prenatal exposure resulted in non-differential exposure misclassification of SHS exposures that attenuated the association between SHS exposure and BMI compared with serum cotinine concentrations. These findings suggest active and secondhand prenatal tobacco smoke exposure may be related to an important public health problem in childhood and later life. In addition, accurate quantification of prenatal secondhand tobacco smoke exposures is essential to obtaining valid estimates.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".