Maternal Smoking and Infant Mortality
Bibliographic record
Abstract
BACKGROUND: Maternal smoking has repeatedly been associated with increased infant mortality rates. No study has investigated whether smoking cessation influences the risk of infant death. This study estimates infant mortality after the second pregnancy in relation to smoking behavior in both the first and the second pregnancy. METHODS: We used the Swedish Medical Birth Register to identify women who delivered their first and second singleton infants during 1983-2002. Maternal smoking during the 2 pregnancies was categorized into (1) never smoker, (2) quitter, (3) starter, and (4) persistent smoker. In the second pregnancy, 555,046 live births (of at least 22 completed gestational weeks) were followed for infant death within 1 year. Cox regression was used to estimate hazard ratios (HRs) with 95% confidence intervals (CIs). RESULTS: Compared with infants born to never smokers, the HR (95% CI) of infant mortality in the second pregnancy was 2.0 (1.7-2.4) among infants born to persistently heavy smokers, whereas among women who stopped smoking in the second pregnancy, the HRs were 1.4 (1.0-2.0) among those who had been heavy smokers in the first pregnancy, and 1.0 (0.8-1.2) among those who had been light smokers. The association of smoking during pregnancy with infant mortality was modified by infant's age, and was strongest at 4-15 weeks after birth. The smoking effect on neonatal mortality, but not postneonatal mortality, was mediated by gestational age. CONCLUSIONS: Smoking cessation reduced the risk of infant death. The smoking-related risk of neonatal mortality appears to be mediated by smoking effects on gestational age, a factor that only partly explains the association between smoking and postneonatal mortality.
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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 | 0.001 | 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".