Effect of Smoking Behavior before and during Pregnancy on Selected Birth Outcomes among Singleton Full-Term Pregnancy: A Murmansk County Birth Registry Study
Bibliographic record
Abstract
The aim of our study was to assess associations between smoking behavior before and during pregnancy and selected adverse birth outcomes. This study is based on the Murmansk County Birth Registry (MCBR). Our study includes women who delivered a singleton pregnancy after 37 weeks of gestation (N = 44,486). Smoking information was self-reported and assessed at the first antenatal visit during pregnancy. We adjusted for potential confounders using logistic regression. The highest proportion of infants with low values of birth weight, birth length, head circumference, ponderal index and of the Apgar score at 5 min was observed for women who smoked both before and during pregnancy. We observed a dose-response relationship between the number of cigarettes smoked per day during pregnancy and the odds of the aforementioned adverse birth outcomes; neither were there significant differences in their occurrences among non-smokers and those who smoked before but not during pregnancy. Moreover, smoking reduction during pregnancy relative to its pre-gestation level did not influence the odds of the adverse birth outcomes. Our findings emphasize a continued need for action against tobacco smoking during pregnancy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".