[INTERVENTIONS FOR SUPPORTING WOMEN TO STOP SMOKING IN PREGNANCY].
Bibliographic record
Abstract
INTRODUCTION: Smoking during pregnancy is a public health problem because of the many adverse effects associated with it. These include intrauterine growth restriction, placenta previa, abruptio placentae, decreased maternal thyroid function, preterm premature rupture of membranes, low birth weight, perinatal mortality, and ectopic pregnancy. An estimated 5-8% of pre-term deliveries, 13-19% of term deliveries of infants with low birth weight, 23-34% cases of sudden infant death syndrome (SIDS), and 5-7% of preterm-related infant deaths can be attributed to prenatal maternal smoking. The risks of smoking during pregnancy extend beyond pregnancy-related complications. Children born to mothers who smoke during pregnancy are at an increased risk of asthma, infantile colic, and childhood obesity. Cigarette smoking and tobacco use during pregnancy have been associated with adverse pregnancy outcomes, including spontaneous pregnancy loss, placental abruption, preterm delivery and low birth weight. In addition, smoking during pregnancy impacts fetal and neonatal development, increase infections rate and is associated with an increased risk for long term pediatric cardiovascular morbidity of the offspring. Identifying maternal tobacco product use allows for targeted interventions. Cessation of tobacco use and prevention of secondhand smoke exposure are key clinical intervention strategies during pregnancy and are recommended by obstetrical guidelines. Inquiry into tobacco use and smoke exposure should be a routine part of the prenatal visit and clinicians should provide pregnancy-tailored counseling for those who smoke. National guidelines from Australia, the UK, New Zealand and Canada recommend the use of nicotine replacement therapy (NRT) by pregnant women who have been unable to quit smoking without medication. According to the American College of Obstetrics and Gynecology, nicotine replacement therapy use in pregnancy has not been sufficiently evaluated to determine safety or efficacy and should only be used under supervision, after a risk benefit analysis. The aim of this review is to provide an overview of current guidelines regarding NRT use in pregnancy, considering the existing evidence base on safety, efficacy and effectiveness.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.077 | 0.014 |
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".