Hair Nicotine: Cotinine Metabolic Ratio in Pregnant Women: A New Method to Study Metabolism in Late Pregnancy
Bibliographic record
Abstract
A large number of smoking women cannot quit their habit when they become pregnant. Preliminary evidence suggests an enhanced nicotine clearance rate in late pregnancy. To evaluate the change in nicotine metabolism in late pregnancy, we undertook a prospective cohort study of a large, diverse group of pregnant women recruited from four Montreal maternal hospitals. Smoking histories were obtained by structured questionnaires administered at 24 to 26 weeks of gestation and postpartum. Hair concentrations of nicotine and cotinine were measured by immunoassays for each trimester based on sectioning the hair and assuming average hair growth of 1 cm per month. A strong correlation was observed between average number of cigarettes smoked per day and hair nicotine and cotinine in all three trimesters. A significant decrease in hair nicotine was observed among steady smokers from the first to third trimester paralleled by a significant increase in hair cotinine. The ratio of hair nicotine:cotinine decreased significantly from the first to the third trimester. Increased nicotine metabolism in late pregnancy results in lower systemic exposure to nicotine. This phenomenon may explain why many pregnant women feel the urge to continue smoking and why standard-dose nicotine replacement therapy has not been effective in reducing smoking during pregnancy in several clinical trials.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".