Cervicovaginal foetal fibronectin in predicting success of induced labour among nulliparous women: a systematic review
Bibliographic record
Abstract
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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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".