Prevalence of smoking during pregnancy in the Republic of the Congo: Maternal smoking is associated with increased risk of prenatal alcohol exposure
Bibliographic record
Abstract
Williams, A., Nkombo, Y., Nkodia, G., Leonardson, G., & Burd, L. (2014). Prevalence of smoking during pregnancy in the Republic of the Congo: Maternal smoking is associated with increased risk of prenatal alcohol exposure. The International Journal Of Alcohol And Drug Research, 3(1), 105-111. doi:10.7895/ijadr.v3i1.131Aims: Development of useful estimates of rates of maternal smoking during pregnancy, and the impact of smoking on rates and duration of maternal alcohol use during pregnancy.Design: A prospective study utilizing systematic screening of consecutive pregnant women.Setting: Ten prenatal care sites in Brazzaville, Congo’s largest city, where 50% of live births in the Congo occur. Women were asked to report the number of cigarettes smoked per day.Findings: From the 10 sites, 3,099 women were screened and 5.5% (n = 172) reported smoking. The mean number of cigarettes smoked per day was 1.1 and only 11% (n = 19) of the women reported smoking two or more cigarettes per day during pregnancy. Smoking during pregnancy was associated with a 4.9-fold increase in prenatal alcohol exposure during pregnancy. We found that 93% of the women who smoked also used alcohol during pregnancy.Conclusions: While the prevalence of smoking and the average number of cigarettes smoked per day were both low, smoking at any level results in a huge increase in risk for maternal alcohol use during pregnancy. The trend across the developing world is for increasing rates of smoking among women and children. Since the number of cigarettes smoked per day was low, smoking cessation programs and public health warnings may be useful in further reducing rates of smoking during pregnancy and, thus, risk for prenatal alcohol exposure in the Congo. We believe this is the first report quantifying the risk of smoking and prenatal alcohol use in a population of pregnant women.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".