Reference Values for Hair Cotinine as a Biomarker of Active and Passive Smoking in Women of Reproductive Age, Pregnant Women, Children, and Neonates: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Exposure to environmental tobacco smoke (ETS) is most often estimated using questionnaires, but they are unreliable. Biomarkers can provide valid information on ETS exposure, the preferred biomarker being cotinine. However, no reference range of hair cotinine exists to distinguish among active, passive, and unexposed nonsmokers. This study identifies cutoffs to validate cotinine as a marker for exposure to ETS. Data were obtained from six databases (four US, one Canada, one France). Active smoking and exposure to ETS were measured in the hair of women of reproductive age, pregnant women, their children, and neonates. Subjects were classified into active smokers, passively exposed to ETS, and unexposed nonsmokers. A total of 1746 cases were available for analysis. For active smokers, mean hair cotinine concentrations (95% confidence interval) were 2.3 to 3.1 ng/mg for nonpregnant women and 1.5 to 1.9 ng/mg for pregnant women. In the group of passive smokers, mean hair cotinine concentrations were 0.5 to 0.7 ng/mg for nonpregnant women, 0.04 to 0.09 ng/mg for pregnant women, 0.9 to 1.1 for children, and 1.2 to 1.7 for neonates. Among unexposed nonsmokers, mean hair cotinine was 0.2 to 0.4 ng/mg in nonpregnant women, 0.06 to 0.09 ng/mg in pregnant women, and 0.3 to 0.4 ng/mg in children. Cutoff values for hair cotinine were established to distinguish active smokers from passive or unexposed (0.8 ng/mg for nonpregnant women and 0.2 ng/mg for pregnant women). A cutoff value of 0.2 ng/mg was accurate in discriminating between exposed children and unexposed. These new values should facilitate clinical diagnosis of active and passive exposure to tobacco smoke. Such diagnosis is critical in pregnancy and in a large number of tobacco-induced medical conditions.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.030 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".