Varenicline is More Effective than Nicotine Replacement Therapy During Pregnancy: Findings from the Smoking MUMS (Maternal Use of Medications and Safety) Study
Bibliographic record
Abstract
IntroductionStudies in the general population suggest that varenicline is more effective than nicotine replacement therapy (NRT) for smoking cessation. However, clinical guidelines recommend against the use of varenicline during pregnancy and suggest NRT be used when the expected benefits outweigh the potential risks.
 Objectives and ApproachWe evaluated whether varenicline was more effective than NRT for smoking cessation when used during pregnancy. Routinely-collected records of all births (01/01/2011-12/31/2012) in New South Wales and Western Australia were used to identify a cohort of women who smoked during the first 20 weeks of pregnancy. Pharmaceutical dispensing data were then linked to identify varenicline or NRT dispensing in the first 20 weeks of pregnancy. Smoking cessation was defined as women reported not smoking after the first 20 weeks of pregnancy. Inverse probability of treatment weighting with propensity scores were used to account for differences between the two treatment groups.
 ResultsOverall, 117 women used varenicline and 135 NRT in the first 20 weeks of pregnancy. In the unweighted sample, more women who used varenicline quit smoking after the first 20 weeks than women using NRT (28.2% vs. 11.1%, crude rate difference:17.1%, 95% confidence intervals[CI]:7.4-26.8%). In the weighted sample, quitting rate was 12.7% (95%CI:0.8-24.6%) higher in pregnant smokers who used varenicline (27.4% vs. 14.7%) when compared to those who used NRT.
 Conclusion/ImplicationsPregnant smokers using varenicline were more likely to quit smoking than those using NRT. This information will assist healthcare providers to make informed recommendations, but data regarding safety of varenicline in pregnancy are also urgently needed. Future studies with greater statistical power are required to confirm our results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".