Use of Medications by Breastfeeding Women in the 2015 Pelotas (Brazil) Birth Cohort Study
Bibliographic record
Abstract
Background: This study describes medication use by women up to 3 months postpartum and evaluates the association between medication use by women who were still breastfeeding at 3 months postpartum and weaning at 6 and 12 months. Methods: Population-based cohort, including women who breastfed (n = 3988). Medications were classified according to Hale’s lactation risk categories and Brazilian Ministry of Health criteria. Duration of breastfeeding was analysed using Cox regression models and Kaplan-Meier curves, including only women who were still breastfeeding at three months postpartum. Results: Medication use with some risk for lactation was frequent (79.6% regarding Hale’s risk categories and 12.3% regarding Brazilian Ministry of Health criteria). We did not find statistically significant differences for weaning at 6 or 12 months between the group who did not use medication or used only compatible medications and the group who used medications with some risk for lactation, according to both criteria. Conclusions: Our study found no association between weaning rates across the different breastfeeding safety categories of medications in women who were still breastfeeding at three months postpartum. Therefore, women who took medications and stopped breastfeeding in the first three months postpartum because of adverse side-effects associated with medications could not be addressed in this analysis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".