In utero exposure to tobacco smoke and subsequent reduced fertility in females
Bibliographic record
Abstract
BACKGROUND: Animal studies have shown that in utero exposure to chemicals in tobacco smoke reduces female fertility, but epidemiological findings have been inconsistent. METHODS: We examined the association between in utero exposure to tobacco smoke and female fertility among women in the Norwegian Mother and Child Cohort Study, enrolled from 1999 to 2007. Around the 17th week of pregnancy, participants reported how long they took to conceive (time to pregnancy), and whether their mother smoked while pregnant with the participant. This analysis included 48 319 planned pregnancies among women aged 15-44 years. We estimated fecundability odds ratios (FORs) using a discrete-time survival analysis, adjusting for age, education and adult tobacco smoking. RESULTS: The adjusted FOR for in utero exposure to tobacco smoke among all subjects was 0.96 [95% confidence interval (CI): 0.93, 0.98], among subjects reporting no adult tobacco smoking or passive exposure it was 0.96 (95% CI: 0.93, 0.99) and among subjects reporting adult tobacco smoking or passive exposure it was 0.95 (95% CI: 0.91, 0.99). We performed a probabilistic sensitivity analysis to estimate the effect of exposure and outcome misclassification on the results, and, as expected, the association became more pronounced after taking misclassification into account. CONCLUSIONS: This large cohort study supports a small-to-modest association between in utero exposure to tobacco smoke and reduced fertility.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".