Breastfeeding duration is positively associated with decreased smoking relapse in the postpartum period
Bibliographic record
Abstract
INTRODUCTION: A significant proportion of women quit smoking during pregnancy, creating a unique window of opportunity to encourage long-term smoking cessation. Efforts to prevent smoking relapse in the postpartum period, however, have largely been ineffective. We investigated the association between breastfeeding duration and smoking relapse to help inform postpartum smoking cessation strategies. METHODS: The Ulm SPATZ Health Study consists of 1006 newborns and their 970 mothers recruited from April 2012 to May 2013 in Ulm, Germany. For this analysis, only mothers who quit smoking during pregnancy, for whom breastfeeding and smoking data were available, were included. Kaplan-Meier plots were used to investigate the relationship between breastfeeding duration and postpartum smoking relapse within 2 years. Cox proportional hazards regression models were used to estimate hazard ratios, adjusted for factors reported to influence postpartum smoking outcomes. RESULTS: A total of 115 mothers were included. They had a mean age of 32.0 (SD: 5.0) years and breastfed for 5.6 (SD: 4.4) months. Of those who remained in the study, 14 (12.2%) experienced smoking relapse by 6 weeks and 48 (51.1%) relapsed by 2 years. In an adjusted analysis which accounted for age, educational attainment, postpartum weight retention, and gestational weight gain, breastfeeding for at least 6 months was significantly associated with decreased smoking relapse within 2 years (HR: 0.18; 95% CI: 0.07 - 0.45). CONCLUSIONS: Breastfeeding for at least 6 months was associated with decreased postpartum smoking relapse. Breastfeeding promotion should be considered to enhance smoking cessation strategies in the postpartum period.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".