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Record W4401316569 · doi:10.32948/ajsep.2024.08.01

Cervicovaginal foetal fibronectin in predicting success of induced labour among nulliparous women: a systematic review

2024· review· en· W4401316569 on OpenAlexaboutno aff
Fawad Inayat, Imad Tariq, Nabiha Bashir, Fawad Ullah, Hadiqa Aimen

Bibliographic record

Venuenot available
Typereview
Languageen
FieldMedicine
TopicPreterm Birth and Chorioamnionitis
Canadian institutionsnot available
Fundersnot available
KeywordsFetal fibronectinMedicinePredictive valueObstetricsGynecologyPsychological interventionInclusion and exclusion criteriaPregnancyInternal medicinePreterm deliveryGestation

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.336
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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