The Prevention of Smoking Relapse in Postpartum Women: A Systematic Review and Network Meta-analysis
Bibliographic record
Abstract
OBJECTIVES: To compare the effects of different interventions for maintaining smoking abstinence in postpartum individuals. METHODS: We searched PubMed, EMBASE, CENTRAL, CINAHL, PsycINFO, and ProQuest up to February 2024. Randomized controlled trials (RCTs) that studied the effects of any interventions on maintaining smoking abstinence in postpartum individuals who quit smoking before delivery were included. A frequentist network meta-analysis using a random-effect model was performed to compare the efficacy of interventions cognitive behavioral therapy (CBT) and motivational interviewing (MI). The surface under the cumulative ranking curve was used to rank the intervention effects. The GRADE approach assessed evidence certainty. RESULTS: We included 11 studies from 10 RCTs (3365 participants). Comparisons with standard care revealed that CBT [relative risk (RR) = 1.03; 95% CI: 0.86, 1.19], CBT-MI (RR = 1.41; 95% CI: 0.87, 2.27), and MI (RR = 1.06; 95% CI: 0.90, 1.24) failed to maintain smoking abstinence at 12 months postpartum. The absolute differences were imprecise, with wide CIs encompassing both potential increases and decreases in smoking abstinence: 7 more per 1000 (95% CI: -31, 43) for CBT, 92 more per 1000 (95% CI: -29, 284) for CBT-MI, and 13 more per 1000 (95% CI: -22, 54) for MI, all with moderate certainty evidence. Subgroup analyses for follow-up periods of <12 months indicated that CBT-MI (RR = 1.67; 95% CI: 1.08, 2.60) and MI (RR = 1.16; 95% CI: 1.01, 1.33) may improve the maintenance of smoking abstinence over the short term. CONCLUSIONS: CBT-MI and MI appear promising in improving the maintenance of smoking abstinence within 12 months postpartum, though further research is needed to enhance long-term abstinence.
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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 | 0.022 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.033 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".