Environmental tobacco smoke exposure and perinatal outcomes: a systematic review and meta‐analyses
Bibliographic record
Abstract
BACKGROUND: While active maternal tobacco smoking has well established adverse perinatal outcomes, the effects of passive maternal smoking, also called environmental tobacco exposure (ETS), are less well studied and less consistent. OBJECTIVE: To determine to the effect of ETS on perinatal outcomes. SEARCH STRATEGY: Medline, EMBASE and reference lists were searched. SELECTION CRITERIA: Studies comparing ETS-exposed pregnant women with those unexposed which adequately addressed active maternal smoking. DATA COLLECTION AND ANALYSIS: Two reviewers independently assessed titles, abstracts, full studies, extracted data and assessed quality. Dichotomous data were pooled using odds ratios (OR) and continuous data with weighted mean differences (WMD) using a random effects model. MAIN RESULTS: Seventy-six articles were included with a total of 48,439 ETS-exposed women and 90,918 unexposed women. ETS-exposed infants weighed less [WMD -60 g, 95% confidence interval (CI) -80 to -39 g], with a trend towards increased low birthweight (LBW, < 2,500 g; RR 1.16; 95% CI 0.99-1.36), although the duration of gestation and preterm delivery were similar (WMD 0.02 weeks, 95% CI -0.09 to 0.12 weeks and RR 1.07; 95% CI 0.93-1.22). ETS-exposed infants had longer infant lengths (1.75 cm; 95% CI 1.37-2.12 cm), increased risks of congenital anomalies (OR 1.17; 95% CI 1.03-1.34) and a trend towards smaller head circumferences (-0.11 cm; 95% CI -0.22 to 0.01 cm). CONCLUSIONS: ETS-exposed women have increased risks of infants with lower birthweight, congenital anomalies, longer lengths, and trends towards smaller head circumferences and LBW.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".