Late preterm birth and previous cesarean section: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Late preterm birth (LPB) is increasingly common and associated with higher morbidity and mortality than term birth. Yet, little is known about the influence of previous cesarean section (PCS) and the occurrence of LPB in subsequent pregnancies. We aim to evaluate this association along with the potential mediation by cesarean sections in the current pregnancy. METHODS: We use population-based birth registry data (2005-2012) to establish a cohort of live born singleton infants born between 34 and 41 gestational weeks to multiparous mothers. PCS was the primary exposure, LPB (34-36 weeks) was the primary outcome, and an unplanned or emergency cesarean section in the current pregnancy was the potential mediator. Associations were quantified using propensity weighted multivariable Poisson regression, and mediating associations were explored using the Baron-Kenny approach. RESULTS: The cohort included 481,531 births, 21,893 (4.5%) were LPB, and 119,983 (24.9%) were predated by at least one PCS. Among mothers with at least one PCS, 6307 (5.26%) were LPB. There was increased risk of LPB among women with at least one PCS (adjusted Relative Risk (aRR): 1.20 (95%CI [1.16, 1.23]). Unplanned or emergency cesarean section in the current pregnancy was identified as a strong mediator to this relationship (mediation ratio = 97%). CONCLUSIONS: PCS was associated with higher risk of LPB in subsequent pregnancies. This may be due to an increased risk of subsequent unplanned or emergency preterm cesarean sections. Efforts to minimize index cesarean sections may reduce the risk of LPB in subsequent pregnancies.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".