Effect of prior cesarean delivery on neonatal outcomes
Bibliographic record
Abstract
AIMS: To examine the effect of a prior cesarean delivery on neonatal outcomes. METHODS: We conducted a retrospective cohort study on all women with a prior livebirth who delivered at the Royal Victoria Hospital between 2001 and 2006. We defined our exposure as a positive history for cesarean delivery and used unconditional logistic regression analysis to estimate the adjusted effect of a previous cesarean delivery on adverse neonatal outcomes. RESULTS: A total of 18,673 births took place of which 9708 were in women with a prior livebirth (77.0% with no previous cesarean delivery and 23.0% with a previous cesarean delivery). As compared to newborns delivered by mothers with no prior cesarean delivery, increasing number of prior cesarean deliveries was associated with an increasing risk of preterm birth [odds ratio (OR) 1.23, 95% confidence interval (CI) 1.09-1.39]; respiratory distress syndrome (OR 3.54, 95% CI 2.02-5.91); and admission to the neonatal intensive care unit (OR 1.41, 95% CI 1.25-1.60). These findings were predominantly due to differences in gestational age and mode of delivery. CONCLUSION: Having a prior cesarean delivery is associated with an increased risk of adverse neonatal outcomes. Adverse neonatal outcomes in subsequent pregnancies is additional evidence to suggest that unless specifically indicated, cesarean delivery should be avoided.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".