Neonatal Outcomes Following Elective Induction of Labor at 39 Weeks: A Systematic Review
Bibliographic record
Abstract
Elective induction of labor (eIOL) at 39 weeks of gestation has gained prominence in obstetric practice, yet its impact on neonatal outcomes remains debated. This systematic review aimed to synthesize evidence on neonatal outcomes following eIOL at 39 weeks compared to expectant management, addressing critical knowledge gaps to inform clinical decision-making. Following PRISMA guidelines, a comprehensive search of PubMed, Scopus, Web of Science, and Embase identified 16 eligible studies. Inclusion criteria focused on low-risk pregnancies undergoing eIOL at 39 weeks, with neonatal outcomes as primary endpoints. Risk of bias was assessed using the Newcastle-Ottawa Scale for cohort studies and Cochrane RoB 2 for the RCT. Narrative synthesis was performed due to heterogeneity. Key findings demonstrated that eIOL at 39 weeks was associated with reduced cesarean delivery rates and lower perinatal mortality, without significant increases in adverse neonatal outcomes. However, subgroup analyses revealed variability: obese women benefited from reduced macrosomia and NICU admissions, while women with prior cesareans faced higher failed TOLAC rates. The RCT confirmed lower cesarean rates but no reduction in composite neonatal morbidity. eIOL at 39 weeks is a safe and effective strategy for reducing cesarean deliveries and perinatal mortality in low-risk populations, though benefits vary by subgroup. Shared decision-making, tailored to maternal characteristics, is essential. Future research should prioritize RCTs in high-risk populations and long-term neonatal follow-up.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".