Pharmacological Treatment for Pregnant Women who Smoke Cigarettes
Bibliographic record
Abstract
Smoking has been associated with several concerns in pregnancy including miscarriage, preterm delivery and stillbirth. Unfortunately, approximately 12% of the pregnant population continue to smoke cigarettes, suggesting a need for additional therapy beyond behavioural change. This paper reviews the literature on the use of nicotine replacement therapy and bupropion (Zyban(R)) in the pregnant human population, the pharmacokinetics of nicotine in the pregnant woman, and current guidelines for smoking cessation for pregnant patients. There are currently four studies that have investigated the use of nicotine patch, three for nicotine gum, and registry and preliminary reports for bupropion. These studies did not show any adverse pregnancy outcomes with the use of pharmacological aid for smoking cessation. All the nicotine replacement therapy studies, with the exception of one randomized-controlled nicotine patch trial had small sample sizes and looked at short-term use of drug in the third trimester. Two studies have examined the pharmacokinetics of nicotine in the pregnant woman. The results from these studies reveal greater nicotine metabolism in pregnant individuals who continue to smoke during pregnancy. Current guidelines from several organizations uniformly recommend that Nicotine Replacement Therapy should be considered if non-pharmacological therapies have been unsuccessful. Bupropion is recommended in pregnancy if the benefits outweigh the risks. There is a need for further studies on the safety and effectiveness of Nicotine Replacement therapy and bupropion in pregnancy. However, considering the current research and guidelines, pharmacological cessation aids should be considered if non-pharmacological therapies have not been effective.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".