Exposure to second-hand smoking as a predictor of fetal loss: Egypt Demographic and Health Survey 2014
Bibliographic record
Abstract
BACKGROUND: Exposure to tobacco smoking during pregnancy has been shown to be associated with elevated risk of adverse pregnancy outcomes such as miscarriage and stillbirth. However, little is known regarding the association between passive smoking and birth outcomes. This study aims to measure the prevalence of passive smoking and assess its relationship with adverse birth outcomes. METHODS: Self-reported birth outcomes (stillbirth/miscarriage/abortion) was the dependent variable that was regressed against self-reported exposure to household smoking along with various individual and community-level factors. We used propensity score matching to identify the sample and used regression analysis to quantify the association between passive smoking and birth outcomes. Sensitivity analysis was conducted to check for the robustness of the associations. RESULTS: Of the 5540 women studied, about half (50.3%, 95% CI=49.3-51.3) reported being exposed to smoking by household members. The prevalence of stillbirth was 14.6% (95% CI=13.9-15.3). In the logistic regression analysis, the confounder-adjusted OR of stillbirth in relation to exposure to smoking was 1.321 (95% CI=1.150-1.517). In the subgroup analysis, we found that the association was significant among certain age groups only. CONCLUSION: The findings of the present study imply a mildly positive association between the occurrence of stillbirth and exposure to smoking in the household.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".