Cigarette smoking during pregnancy: comparison of biomarkers for inclusion in epidemiological studies
Bibliographic record
Abstract
Prenatal exposure to tobacco smoke represents an important confounding factor in epidemiological studies addressing developmental effects and requires careful controlling by the use of biomarkers. We compared the following biomarkers of exposure to tobacco smoke during pregnancy and related biological effects in 23 smokers and 17 non-smokers: placental concentrations of heavy metals (cadmium, chrome, lead and zinc), cotinine concentration in meconium, placental CYP1A1 activity (EROD) and bulky DNA adducts. Cadmium was detected in all samples and found in higher concentration in placentas of smokers compared with non-smokers (geometric mean ± GSD: 56.1 ± 1.8 vs 27.4 ± 1.6 μg kg -1 dry weight; p < 0.001). Cotinine was not detected in meconium samples from the non-smoker group, while samples from the smoker group contained a mean concentration of 114.1 ± 2.9 μg kg -1 . Correlation analysis of biomarkers among smokers revealed that daily cigarette consumption was strongly correlated to placental cadmium (Pearson's r = 0.83, p < 0.001) and to cotinine (r = 0.73, p < 0.001). EROD activity was also higher in smokers than in non-smokers (9.4 ± 3.4 vs 2.5 ± 1.8 pmol resorufin min -1 mg -1 protein; p < 0.001) and values were correlated to cotinine concentration in meconium (r = 0.80, p < 0.001) and placental cadmium level (r = 0.66, p < 0.001). The amount of bulky DNA adducts in placenta was highly variable and poorly associated with smoking status. Because of their high sensitivity and specificity to detect women who smoke during pregnancy, cotinine concentrations in meconium and placental EROD activity should be incorporated in epidemiological studies that investigate adverse developmental effects induced by in utero exposure to environmental contaminants.
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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 | 0.040 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".