Effect of previous caesarean section on reproductive and pregnancy outcomes after assisted reproductive technology: A systematic review and meta‑analysis
Bibliographic record
Abstract
Pregnancies following previous caesarean section (CS) are associated with higher incidence of infections, postpartum haemorrhage and obstetric complications. The present study aimed to explore the effect of previous CS on reproductive, maternal and neonatal outcomes in women who underwent assisted reproductive techniques (ART). A systematic review and meta-analysis were conducted to assess reproductive and pregnancy outcomes following ART in women with and without a previous CS. Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines were followed. Eligible language articles written in English, published up to October 2023, were identified in Medline, Google Scholar and Science Direct databases. The quality of the included studies was assessed using the Newcastle Ottawa Scale. A total of 19 articles, reporting on 13 different outcomes met the inclusion criteria. It was revealed that women with previous CS had 9% lower clinical pregnancy rates, 13% lower live birth rates, 11% lower implantation rates and 28% lower multiple pregnancy rates compared with women who had prior natural vaginal deliveries. Additionally, previous CS was associated with an 8-fold higher risk of difficult embryo transfers. No significant differences were noted in ectopic pregnancy rates, miscarriage rates or biochemical pregnancy rates. The present systematic review and meta-analysis demonstrated that previous CS is associated with decreased prospects of clinical pregnancy, live birth and successful embryo implantation during ART. The findings of the present study underscored the need to counsel women with prior CS regarding its potential impact on ART outcomes.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.032 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".