Hair Biomarkers as Measures of Maternal Tobacco Smoke Exposure and Predictors of Fetal Growth
Bibliographic record
Abstract
INTRODUCTION: Most biomarker studies of the effects of maternal smoking on fetal growth have been based on a single blood or urinary cotinine value, which is inadequate in capturing maternal tobacco exposure over the entire pregnancy. We used hair biomarkers to compare the associations of maternal self-reported smoking, hair nicotine, and hair cotinine with birth weight for gestational age (BW for GA) among active and passive smokers during pregnancy. METHODS: We collected maternal hair in the immediate postpartum period and measured nicotine and cotinine concentrations averaged over the pregnancy in 444 term controls drawn from 5,337 participants in a multicenter nested case-control study of preterm birth. BW for GA Z-score and small for gestational age (SGA) were based on Canadian population-based standards. RESULTS: The addition of hair nicotine to multiple linear regression models containing self-reported active smoking, hair cotinine, or both explained significantly more variance in the BW for GA Z-score (p = .01, .03 and .04, respectively). Similarly, women with hair nicotine, but not cotinine, at or above the median value had a significant increase in the risk of SGA birth (odds ratio: 3.07, 95% CI: 1.25-7.52). No significant association was observed between maternal passive smoking and BW for GA based on hair biomarkers. CONCLUSIONS: Hair nicotine is a better predictor of reductions in BW for GA than either hair cotinine or self-report. Our negative results for passive smoking suggest that previously reported small but significant effects may be explained by misclassification of active smokers as passive smokers based on self-report.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".