Background The global rates of labor induction continue to exhibit a surge, attributed to a range of medical, obstetric, and non-medical factors. Although the Bishop score is often used to assess cervical preparation, its ability to accurately predict outcomes, particularly in nulliparous women with an unfavorable cervix, is still unknown. Method A complete review of the literature was undertaken, including PubMed, EMBASE, Cochrane Library, and Google Scholar databases, with the search period extending until April 2023. The studies included in this analysis focused on investigating the predictive value of fFN concerning induced labor outcomes in nulliparous women. The process of data extraction primarily concentrated on the features of the study, interventions, controls, criteria for inclusion and exclusion, and the outcomes that were evaluated. The quality of the included studies was assessed using the Newcastle-Ottawa Scale. Results The review synthesized findings from five studies, revealing varied predictive values of fFN. Sciscione et al. (2005) reported no significant difference in vaginal delivery rates between positive and negative fFN groups (Positive fFN: 55.8% vs. Negative fFN: 53.3%; P > .70). Uygur et al. (2016) found a higher cesarean section rate in patients with negative fFN results (P = 0.002). Reis et al. (2003) highlighted that higher parity and Bishop scores were more predictive than fFN alone (P = .021 for funneling; P = .157 for fFN presence). Grab et al. (2022) and Khalaf et al. (2023) further corroborated fFN's role in predicting labor outcomes, with the latter study demonstrating high sensitivity (85%), specificity (80%), and accuracy (82.6%) in predicting successful labor induction (P < .05 for Bishop score relation with fFN; P = 0.029 for positive vs. negative fFN). Conclusion This systematic review validated that fFN is a significant biomarker for predicting labor induction outcomes, especially in nulliparous women. The combination of additional clinical factors with fFN has been shown to boost its prediction accuracy, indicating the need for a personalized strategy to labor induction.
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