ReviewInternational journal of women's health2023
Preterm Birth: Screening and Prediction.
Review in International journal of women's health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Global and population-specific association of MTHFR polymorphisms with preterm birth risk: a consolidated analysis of 44 studies.BMC pregnancy and childbirth · 2025Pooled it
- Large-Pore Channels at the Maternal-Fetal Interface: Progress and Open Research Avenues.Biology · 2026Review
- The Predictive Power of Early Socio-Emotional Skills on Behavioral Outcomes in Very Preterm Preschoolers: A Longitudinal Study.Pediatric reports · 2026Article
- Efficacy of Combined Cervical Pessary and Progesterone in Women at High-Risk of Preterm Birth.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial Intelligence and Machine Learning for predicting Major Obstetric Emergencies: Current Evidence, Clinical Translation, and Future Directions.International journal of women's health · 2026Review
- Cervical Elastography as a Predictive Tool for Preterm Birth: A Systematic Review and Meta-analysis.Cureus · 2025Review
- Expression and Biological Activity Analysis of Recombinant Fibronectin3 Protein inBiotech (Basel (Switzerland)) · 2025Article
- Article
- A Multi-Algorithm Machine Learning Model for Predicting the Risk of Preterm Birth in Patients with Early-Onset Preeclampsia.International journal of general medicine · 2025Article
- Evidence integration: The transformative role of artificial intelligence in maternal health.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Preterm birth (PTB) affects approximately 10% of births globally each year and is the most significant direct cause of neonatal death and of long-term disability worldwide. Early identification of women at high risk of PTB is important, given the availability of evidence-based, effective screening modalities, which facilitate decision-making on preventative strategies, particularly transvaginal sonographic cervical length (CL) measurement. There is growing evidence that combining CL with quantitative fetal fibronectin (qfFN) and maternal risk factors in the extensively peer-reviewed and validated QUanititative Innovation in Predicting Preterm birth (QUiPP) application can aid both the triage of patients who present as emergencies with symptoms of preterm labor and high-risk asymptomatic women attending PTB surveillance clinics. The QUiPP app risk of delivery thus supports shared decision-making with patients on the need for increased outpatient surveillance, in-patient treatment for preterm labor or simply reassurance for those unlikely to deliver preterm. Effective triage of patients at preterm gestations is an obstetric clinical priority as correctly timed administration of antenatal corticosteroids will maximise their neonatal benefits. This review explores the predictive capacity of existing predictive tests for PTB in both singleton and multiple pregnancies, including the QUiPP app v.2. and discusses promising new research areas, which aim to predict PTB through cervical stiffness and elastography measurements, metabolomics, extracellular vesicles and artificial intelligence.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.