Intrapartum Synthetic Oxytocin and Its Effects on Maternal Well‐Being at 2 Months Postpartum
Bibliographic record
Abstract
BACKGROUND: Synthetic oxytocin (synOT) is commonly used in labor management to induce and augment labor, and to prevent postpartum hemorrhage. However, its long-term consequences for maternal health and behavior are largely understudied. We examined the relationship between synOT and maternal oxytocin levels, breastfeeding, and maternal mental health at 2 months postpartum. METHODS: Women were recruited during pregnancy or within 48 hours of giving birth through obstetric practices and hospitals. A total of 386 women were visited in their homes at 2 months postpartum, where they completed questionnaires assessing breastfeeding, depression, anxiety, posttraumatic stress, and somatization. Oxytocin levels were obtained from blood samples and synOT dosage information was gathered from hospital charts. RESULTS: Intrapartum synOT dose was positively correlated with endogenous oxytocin levels at 2 months postpartum. Women who were exclusively breastfeeding at 2 months postpartum had received significantly less synOT compared with their nonexclusively breastfeeding counterparts. Higher synOT dose was associated with greater depressive, anxious, and somatization symptoms. SynOT dose was not associated with perinatal posttraumatic stress. CONCLUSIONS: The widespread use of synOT in managed labor warrants caution, as the influence of synOT on a new mother's well-being is evident at 2 months postpartum.
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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 | 0.003 |
| 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 | 0.000 | 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".