Cesarean Section and Subsequent Stillbirth, Is Confounding by Indication Responsible for the Apparent Association? An Updated Cohort Analysis of a Large Perinatal Database
Bibliographic record
Abstract
BACKGROUND: Several studies and a recent meta-analysis have suggested that previous Cesarean section may increase the risk of stillbirth in a subsequent pregnancy. Given the high rates of Cesarean section in contemporary obstetric practice, this is of considerable public health importance. We sought to evaluate the potential that this association is the result of residual confounding bias. METHODS: A large perinatal database (Alberta Perinatal Health Project) was searched to identify a matched set of first and second births from the years 1992-2006. Data on pregnancy outcomes, demographics and potential confounding factors were obtained. RESULTS: The cohort was comprised of 98538 matched first and second births. Multivariate analysis did not reveal an association between previous Cesarean section and stillbirth, OR = 1.38 (0.98, 1.93). Restricting the analysis to a low risk group further attenuated the association, OR = .99 (0.62, 1.52). Analysis of the risk by indication for Cesarean section found that the risk was not increased for previous dystocia, OR = .91 (0.53, 1.55) nor for breech presentation, OR = 1.06 (0.50, 2.28) but only for other indications including non reassuring fetal status and fetal distress, OR = 1.96 (1.29, 2.98). CONCLUSIONS: The results of our cohort analysis suggest that previous Cesarean section does not cause an increased risk of stillbirth.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".